Communication method, device and system and storage medium

Abstract

The invention provides a communication method, a communication device, a communication system and a storage medium, and the method can be applied to a communication and sensing integrated scene exemplarily. In the method, a first core network element receives a sensing task sent by a second core network element, and the sensing task comprises a service quality demand. And the first core network element determines a sensing strategy according to the service quality requirement, and the sensing strategy is used for indicating to select a sensing mode and / or a sensing node for the sensing task. And the first core network element determines a sensing node and / or a sensing mode matched with the sensing task according to the sensing strategy. Wherein the sensing node is used for executing the sensing task, and the sensing mode is used for indicating the mode of executing the sensing task by the sensing node. Therefore, through the method, the matched sensing node and / or sensing mode can be determined for the sensing task according to the service quality requirement of the sensing task so as to meet the requirement of the sensing task.

Description

Technical Field

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

[0002] Integrated sensing and communication (ISAC) technology combines communication and sensing functions, enabling wireless networks to not only transmit data but also perceive their surroundings. Communication refers to the transmission of information between two or more points, while sensing involves detecting parameters of the physical environment, such as speed measurement and target localization. ISAC has become a core technology for improving spectrum utilization and supporting multifunctional applications. ISAC aims to deeply integrate sensing and communication functions on a single hardware platform to meet diverse needs such as target localization, environmental monitoring, and dynamic tracking. Currently, 3GPP has defined several integrated sensing modes, which refer to operating methods. For example, sensing mode 1 corresponds to base station self-transmission and self-reception; sensing mode 2 corresponds to base station A transmitting and base station B receiving; sensing mode 3 corresponds to base station transmitting and terminal device receiving; sensing mode 4 corresponds to terminal device transmitting and base station receiving; sensing mode 5 corresponds to terminal device self-transmission and self-reception; and sensing mode 6 corresponds to terminal device A receiving and terminal device B receiving, etc.

[0003] Typically, the foundation for achieving inter-sensing communication lies in determining the sensing nodes and sensing modes. The selection of sensing nodes and sensing modes is the core issue in realizing inter-sensing communication. Sensing nodes refer to the set of physical or logical nodes that undertake sensing / communication tasks, including sensing transmitting nodes and sensing receiving nodes. The selection of sensing nodes determines the observable signal sources and geometric conditions, which can affect positioning accuracy, coverage, etc.; the selection of sensing modes determines the measurement type and resource consumption (for example, high-frequency active detection improves accuracy but increases overhead).

[0004] Currently, many perception tasks (such as train cooperative braking and autonomous driving obstacle avoidance) are extremely sensitive to indicators such as perception response latency and perception accuracy. How to configure reasonable perception modes and / or perception entities for perception tasks is an urgent technical problem to be solved. Summary of the Invention

[0005] This application provides a communication method, apparatus, system, and storage medium capable of allocating matching sensing nodes and / or sensing modes for sensing tasks to meet sensing requirements. In a first aspect, a communication method is provided. This method can be executed by, for example, a first core network element, or by a component (such as a circuit, chip, or chip system) configured in the first core network element, or by a logic module or software capable of implementing all or part of the functions of the first core network element. This application does not limit the scope of this method. The following description uses the first core network element as an example.

[0006] The method includes: a first core network element receiving a sensing task sent by a second core network element, the sensing task including quality of service (QoS) requirements; the second core network element managing the first core network element; the first core network element determining a sensing strategy based on the QoS requirements, the sensing strategy indicating the selection of a sensing mode and / or sensing nodes for the sensing task; and the first core network element determining sensing nodes and / or sensing modes matching the sensing task based on the sensing strategy. The sensing nodes are used to execute the sensing task, and the sensing mode indicates how the sensing nodes execute the sensing task. The sensing nodes matching the sensing task are sensing nodes in the first set of sensing nodes managed by the first core network element. Therefore, this method can determine matching sensing nodes and / or sensing modes for the sensing task based on its QoS requirements to meet the needs of the sensing task.

[0007] Secondly, a communication method is provided. This method can be executed by a second core network element, or by a component (such as a circuit, chip, or chip system) configured in the second core network element, or by a logic module or software capable of implementing all or part of the functions of the second core network element. This application does not limit the scope of this method. The following description uses the second core network element as an example.

[0008] The method includes: a second core network element acquiring a perception task, the perception task including quality of service requirements, the perception task instructing the perception of objects in a target area. The second core network element, based on the perception task, determines a first core network element to execute the perception task and sends the perception task to the first core network element. The quality of service requirements in the perception task instruct the first core network element to determine a perception strategy, the perception strategy instructing the first core network element to select a perception mode and / or perception nodes for the perception task, the perception nodes being used to execute the perception task, the perception mode indicating the manner in which the perception nodes execute the perception task, and the perception nodes being perception nodes in a first set of perception nodes managed by the first core network element.

[0009] The second aspect is the implementation on the network device side, which corresponds to the first aspect. The explanations, supplements, and descriptions of the beneficial effects of the first aspect also apply to the second aspect, and will not be repeated here.

[0010] Thirdly, a communication device is provided, applied to a first core network element, including a processing module and a transceiver module. The transceiver module receives a sensing task sent by a second core network element, the sensing task including quality of service requirements, the second core network element managing the first core network element; the processing module determines a sensing strategy based on the quality of service requirements, the sensing strategy instructing the selection of a sensing mode and / or a sensing node for the sensing task, the sensing node performing the sensing task, and the sensing mode instructing the manner in which the sensing node performs the sensing task; the processing module further determines, based on the sensing strategy, a sensing node and / or a sensing mode matching the sensing task, the sensing node being a sensing node in a first set of sensing nodes managed by the first core network element.

[0011] Fourthly, a communication device is provided, applied to a second core network element, including a transceiver module and a processing module. The transceiver module is used to acquire a sensing task, the sensing task including quality of service requirements, the sensing task instructing the sensing of objects in a target area; the processing module is used to determine a first core network element to execute the sensing task based on the sensing task; the transceiver module is also used to send the sensing task to the first core network element, the quality of service requirements instructing the first core network element to determine a sensing strategy, the sensing strategy instructing the first core network element to select a sensing mode and / or a sensing node for the sensing task, the sensing node being used to execute the sensing task, the sensing mode instructing the sensing node to execute the sensing task in a manner that the sensing node performs the sensing task, and the sensing node being a sensing node in a first set of sensing nodes managed by the first core network element.

[0012] The third and fourth aspects are the implementation on the device side, which correspond to the first and second aspects. The explanations, supplements, and descriptions of the beneficial effects of the first and second aspects also apply to the third and fourth aspects, and will not be repeated here.

[0013] Fifthly, a communication device is provided, including a processor. The processor is coupled to a memory and can be used to execute instructions or data in the memory to implement the method in any possible implementation of the first aspect described above. Optionally, the communication device further includes a memory. Optionally, the communication device further includes a communication interface, and the processor is coupled to the communication interface.

[0014] In one implementation, the communication interface may be a transceiver, or an input / output interface.

[0015] In another implementation, the communication device is a chip configured in a terminal device. When the communication device is a chip configured in a terminal device, the communication interface can be an input / output interface.

[0016] In a sixth aspect, a communication device is provided, including a processor. The processor is coupled to a memory and can be used to execute instructions or data in the memory to implement the method in any possible implementation of the second aspect described above. Optionally, the communication device further includes a memory. Optionally, the communication device further includes a communication interface, and the processor is coupled to the communication interface.

[0017] In one implementation, the communication interface may be a transceiver, or an input / output interface.

[0018] In a seventh aspect, a processor is provided, comprising: an input circuit, an output circuit, and a processing circuit. The processing circuit is configured to receive signals through the input circuit and transmit signals through the output circuit, causing the processor to execute a method in any possible implementation of any aspect.

[0019] In specific implementation, the processor can be one or more chips, the input circuit can be input pins, the output circuit can be output pins, and the processing circuit can be transistors, gate circuits, flip-flops, and various logic circuits. The input signal received by the input circuit can be received and input by, for example, but not limited to, a receiver, and the signal output by the output circuit can be, for example, but not limited to, output to and transmitted by a transmitter. Furthermore, the input circuit and the output circuit can be the same circuit, which is used as both the input circuit and the output circuit at different times. This application does not limit the specific implementation of the processor and various circuits.

[0020] Eighthly, a communication device is provided, including a processor and a memory. The processor is used to read instructions stored in the memory, receive signals via a receiver, and transmit signals via a transmitter to execute the method in any possible implementation of any of the preceding aspects.

[0021] Optionally, the processor may be one or more, and the memory may be one or more.

[0022] Ninthly, a computer program product is provided, the computer program product comprising: a computer program (also referred to as code or instructions) that, when the computer program is run, causes a computer to perform a method in any possible implementation of any of the above aspects.

[0023] In a tenth aspect, a computer-readable storage medium is provided that stores a computer program (also referred to as code or instructions) that, when run on a computer, causes the computer to perform the methods in any possible implementation of any of the preceding aspects.

[0024] Eleventhly, embodiments of this application provide a chip system including one or more processors for calling and executing instructions stored in memory, causing the methods in any of the above aspects or possible implementations to be executed. The chip system may be composed of chips or may include chips and other discrete devices.

[0025] The chip system may include input circuits or interfaces for transmitting information or data, and output circuits or interfaces for receiving information or data.

[0026] In a twelfth aspect, a communication system is provided, including the aforementioned first core network element and second core network element. Optionally, the communication system may further include other devices that communicate with the first core network element and / or the second core network element. Attached Figure Description

[0027] Figure 1 A communication system structure diagram provided in this application embodiment; Figure 2 A flowchart illustrating the interaction of a communication method provided in this application embodiment; Figure 3 A flowchart illustrating the subscription process for sensing capabilities is provided in this application embodiment; Figure 4 A structural diagram of a communication device provided for implementation of this application; Figure 5 This is a structural diagram of another communication device provided in an embodiment of this application. Detailed Implementation

[0028] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0029] The technical solutions provided in this application can be applied to various communication systems, such as: Global System for Mobile Communications (GSM) systems, General Packet Radio Service (GPRS), Wireless Local Area Network (WLAN), Long Term Evolution (LTE) systems, LTE Frequency Division Duplex (FDD) systems, LTE Time Division Duplex (TDD) systems, sidelink communication systems, Universal Mobile Telecommunication System (UMTS), Worldwide Interoperability for Microwave Access (WiMAX) communication systems, non-terrestrial network (NTN) communication systems, 5th generation (5G) mobile communication systems, or new radio access technology (NR). Among these, 5G mobile communication systems can include non-standalone (NSA) and / or standalone (SA) networking. The technical solutions provided in this application can also be applied to future communication systems. This application does not limit the scope of these applications.

[0030] Figure 1 This is a schematic diagram of a communication system 100 used in an embodiment of this application. The communication system 100 may include network devices, such as... Figure 1 The network device 110 is shown. The communication system 100 may also include terminal devices, such as... Figure 1 The terminal device 120 shown. The network device 110 and the terminal device 120 can communicate via a wireless link.

[0031] Figure 1 An exemplary network device 110 and a terminal device 120 are shown. Optionally, the communication system 100 may also include multiple network devices and / or multiple terminal devices.

[0032] The network equipment in this application can be network-side equipment such as access network equipment and core network equipment. Access network equipment is sometimes also called access node. Access network equipment has wireless transceiver capabilities and is used to communicate with terminals. Access network equipment includes, but is not limited to, base stations, evolved NodeBs (eNodeBs), transmission reception points (TRPs) in the aforementioned communication systems, next-generation NodeBs (gNBs) in 5G mobile communication systems, access network equipment or modules of access network equipment in open RAN (ORAN) systems, satellites in NTN communication systems, base stations in future mobile communication systems, or access nodes in WiFi systems. Access network equipment can also be modules or units capable of implementing some of the functions of a base station. Access network equipment can be a macro base station (such as...). Figure 1 The access network device can be a micro base station or indoor station, a relay node or donor node, or a wireless controller in a cloud radio access network (CRAN) scenario. Optionally, the access network device can also be a server, wearable device, or vehicle-mounted device. For example, in vehicle-to-everything (V2X) technology, the access network device can be a roadside unit (RSU). Multiple access network devices in a communication system can be base stations of the same type or different types. Base stations can communicate with terminals directly or via relay stations. Terminals can communicate with multiple base stations using different access technologies. The embodiments of this application do not limit the specific technology or device form used in the access network device. In this application, the access network device is referred to as a network device.

[0033] In this application, the means for implementing the functions of a network device can be a network device itself, or a means capable of supporting the network device in implementing those functions, such as a processor, circuit, chip, or chip system. This means can be installed in or connected to the network device. In the technical solutions provided in this application, the example of a network device being used to implement the functions of a network device is used to describe the technical solutions provided in this application.

[0034] The terminal device in this application can be a wireless terminal device capable of receiving network device scheduling and instruction information. The wireless terminal device can be a device providing voice and / or data connectivity to a user, a handheld device with wireless connectivity, or other processing devices connected to a wireless modem. For example, the terminal device can communicate with one or more core networks or the Internet via a radio access network (RAN). The terminal device can also be referred to as a terminal, user equipment (UE), mobile station, mobile terminal, etc. Terminal devices can be widely used in various scenarios, such as device-to-device (D2D), vehicle-to-everything (V2X) communication, machine-type communication (MTC), Internet of Things (IoT), ultra-reliable low-latency communication (URLLC), virtual reality, augmented reality, industrial control, autonomous driving, telemedicine, smart grids, smart furniture, smart offices, smart wearables, smart transportation, smart cities, or satellite communication, etc. The terminal can be a mobile phone, tablet computer, computer with wireless transceiver capabilities, wearable device, vehicle, aircraft (such as drone, helicopter, airplane), hot air balloon, ship, robot, robotic arm, or smart home device, etc. The embodiments of this application do not limit the form of the terminal device.

[0035] In this application, the apparatus for implementing the functions of a terminal device can be the terminal device itself, or any apparatus capable of supporting the terminal device in implementing those functions, such as a processor, circuit, chip, or chip system. This apparatus can be installed in or connected to the terminal device. In the technical solutions provided in this application, the example of a terminal device being used to implement the functions of a terminal device is used to describe the technical solutions provided in this application.

[0036] Access network devices and / or terminals can be fixed or mobile. They can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; on water; or in the air on aircraft, balloons, and satellites. This application does not limit the application scenarios of the access network devices and terminals. Access network devices and terminal devices can be deployed in the same or different scenarios; for example, both can be deployed on land; or the access network device can be deployed on land, and the terminal device on water, etc., and so on.

[0037] In practical applications, multiple network devices can collaborate to assist terminals in achieving wireless access, with different network devices each implementing a portion of the base station's functions. For example, network devices can be central units (CUs), distributed units (DUs), CUs (control planes, CPs), CUs (user planes, UPs), or radio units (RUs), etc. CUs and DUs can be set up separately or included in the same network element, such as a baseband unit (BBU). RUs can be included in radio frequency equipment or radio frequency units, such as remote radio units (RRUs), active antenna units (AAUs), or remote radio heads (RRHs).

[0038] In different systems, CU (or CU-CP and CU-UP), DU, or RU may have different names, but those skilled in the art will understand their meaning. For example, in an ORAN system, CU can also be called O-CU (Open CU), DU can also be called O-DU, CU-CP can also be called O-CU-CP, CU-UP can also be called O-CU-UP, and RU can also be called O-RU. Any of the units among CU (or CU-CP, CU-UP), DU, and RU in this application can be implemented through software modules, hardware modules, or a combination of software and hardware modules. CU (or CU-CP and CU-UP), DU, and RU can implement different protocol layer functions.

[0039] To facilitate understanding of the embodiments of this application, the terminology used in this application will be briefly explained first. Optionally, the explanation of some terms may also refer to the explanations in the 3rd Generation Partnership Project (3GPP) standard protocol.

[0040] 1. Sensing function (SF) To support the elastic scaling and localized deployment of sensing capabilities, sensing capabilities can be further broken down into sensing and control capabilities. The control function (SCF) and the sensing processing function (SPF).

[0041] It should be noted that whether to merge the control and processing functions into a single network function (NF) or to split them into two NFs, and whether / how to define new interfaces between them, can be determined based on the actual application scenario.

[0042] 2. SPF The primary responsibilities of the SPF include analyzing, transforming, and structuring raw sensed data to generate sensing results that can be used in applications. For example, in a wireless network, an SPF might be deployed on or near the Radio Access Network (RAN) side to receive raw measurement data from sensed entities (such as terminals or non-3GPP equipment) and process it into structured information. 3. SCF SCF is a key component of SF in 6G networks, responsible for performing sensing tasks. It coordinates and manages sensing data. Working in conjunction with sensing processing functions, it enables the generation, control, and optimization of sensing data. In the 6G architecture, the SCF serves two purposes: firstly, it coordinates sensing tasks, specifically by scheduling and allocating resources to ensure efficient integration of sensing operations and communication functions; secondly, it acts as a control interface, specifically as a sensing control function, providing a control interface for sensing entities (such as terminals or non-3GPP devices) and supporting the access and management of multi-source sensing data.

[0043] In this application, the SCF includes the core network SCF (Core-SCF) and the edge SCF (Edge-SCF). Both have sensing, processing, and control functions, but the Core-SCF has a larger control range and can coordinate different Edge-SCFs, providing a global perspective and cross-domain coordination capabilities to increase the unity of local efficiency and global optimization.

[0044] 4. Edge-SCF Deployment Options Edge-SCF as a separate edge application: Edge-SCF is deployed as an independent service on the edge computing platform (edge ​​cloud / MEC). The advantages of this deployment method are strong computing and caching capabilities, easy integration with UPF / local storage or other edge services, and suitability for scenarios requiring significant state maintenance and parallel computing; it also facilitates the management of candidate caches (HotSet / ColdSet) across multiple gNBs / cells. In this deployment method, Core-SCF maintains centralized / cross-domain coordination responsibilities.

[0045] Lightweight RAN-side deployment (Edge-SCF embedded in gNB / RAN): Deploying a lightweight Edge-SCF (or SCF agent) near the gNB or RAN offers advantages such as minimal radio access latency and the fastest local decision-making, making it suitable for scenarios that are extremely latency-sensitive and have a small candidate pool. This deployment is typically limited by computing power and cache capacity, and is suitable for sensing tasks that prioritize rapid local decision-making.

[0046] In practical implementation, for scenarios with high computing requirements and the need to manage multiple cells / multiple gNBs, independent edge instances are preferred to enable caching / parallel computing and integration with other edge services; for tasks with extremely low latency and small candidate size, lightweight RAN deployment is preferred to obtain the lowest wireless latency.

[0047] In real-world systems, hybrid deployments are also possible. For example, gNB-side agents can be used for rapid decision-making in real-time emergency tasks within the same domain, while edge cloud instances can be used in conjunction with Core-SCF to complete cross-domain optimization for complex / large-scale collaborative tasks.

[0048] It should be noted that Edge-SCF or edge SCF specifically refers to the sensing and control function entity deployed on the network access side (e.g., access network / edge computing node, base station edge switching point or other access edge computing infrastructure) and used for localized sensing control, real-time scheduling and local signaling processing.

[0049] Although the Edge-SCF is physically deployed at the edge, in terms of management and control logic, it is managed by the Core-SCF (the actual core network element) and can be regarded as an extension or proxy of the core control functions at the edge. When describing control flow (such as receiving tasks from the Core-SCF), it is categorized under "network-side core functions".

[0050] 5. Local decision-making Applicable conditions: When the candidate sensing entities (also known as sensing nodes) required by the requested sensing task are basically concentrated in the same Edge-SCF jurisdiction, and the resources (time frequency, CPU, energy consumption) of the edge assessment and the estimated response delay meet the task QoS requirements (e.g., the estimated local execution delay ≤ T_local_budget), the local decision-making process is adopted.

[0051] Edge-SCF selects sensing entities locally and directly performs sensing configuration, data collection, and result calculation with the selected sensing entities.

[0052] 6. Overall coordination Applicable conditions: When a sensing task requires collaboration across multiple Edge-SCF domains (e.g., cross-domain target tracking), or when there are insufficient candidate sensing nodes or resources in the current domain, resulting in the inability to meet quality of service (QoS) requirements (e.g., physical resource block (PRB) utilization > ρ_busy), or when the estimated achievable accuracy does not meet the requirements, or when the time budget allows for higher quality global optimization, Core-SCF can initiate or participate in cross-domain joint optimization.

[0053] It should be understood that the technical terms used in this application are for illustrative purposes only and not as limiting. For example, as technology evolves, technical terms may also change, and other technical terms that have the same technical meaning should also apply to this application.

[0054] Currently, different sensing tasks have different service quality requirements. How to allocate sensing nodes and / or sensing modes that can meet the service quality requirements to different sensing tasks is a technical problem that needs to be solved.

[0055] In view of this, this application provides a communication method in which a first core network element receives a sensing task sent by a second core network element, the sensing task including quality of service (QoS) requirements. The first core network element determines a sensing strategy based on the QoS requirements, the sensing strategy indicating the selection of a sensing mode and / or sensing nodes for the sensing task. Then, the first core network element selects sensing nodes and / or sensing modes that match the sensing task according to the sensing strategy. The sensing nodes are sensing nodes in a first set of sensing nodes managed by the first core network element, and the second core network element manages the first core network element. Therefore, the selection and optimization of sensing modes and sensing entities can be dynamically completed according to the QoS requirements of the sensing task (e.g., accuracy, latency, update rate, reliability, etc.) to complete the sensing task while satisfying QoS requirements.

[0056] The solution provided in this application will be described in detail below with reference to the corresponding flowcharts. It is understood that the illustrative flowcharts provided in this application primarily use different devices (e.g., terminal devices, network devices) as examples of the execution subjects of this interactive illustration to illustrate the method, but this application does not limit the execution subjects of the interactive illustrations. For example, the devices (e.g., terminal devices, network devices) in the illustrative flowcharts can also be chips, chip systems, or processors that support the implementation of this method on the device, or logic modules or software that can implement all or part of the functions of the device.

[0057] As a general statement, the message or signaling interactions involved in the interaction process of this application embodiment can be standard messages or signaling or newly introduced messages or signaling. This application embodiment does not make specific limitations on this.

[0058] Figure 2 This is a schematic diagram of a communication method 200 according to an embodiment of this application. It can be understood that... Figure 2 The terminal device in the middle can be Figure 1 Any terminal device in the context of network equipment can refer to any component within that terminal device (such as a processor, chip, or chip system). Network equipment can be... Figure 1 Any access network device, or a component within an access network device (such as a processor, chip, or chip system). Figure 2 As shown, the method 200 includes the following steps: S201: The second core network element acquires perception tasks.

[0059] In this embodiment, the application function (AF) can initiate a sensing task, which can be transmitted to the second core network element, namely the core network sensing and control function (Core-SCF), via the network exposure function (NEF). The sensing task is used to instruct the sensing of targets within the target area.

[0060] In addition to the target information being sensed, the perception task mainly includes two key metadata aspects: service quality requirements and time constraint information. Specifically, service quality requirements define the standards that the perception results must meet, such as perception accuracy (e.g., less than 0.5 meters), update rate (e.g., 100Hz), reliability (e.g., 99.9%), resolution, and resource sensitivity. Among these, resource sensitivity refers to the degree of concern the perception task has regarding resource consumption and energy usage.

[0061] Time constraints define the timeliness requirements of sensing tasks, specifically including execution timing, task duration, and maximum permissible end-to-end latency. Execution timing refers to the start time or triggering condition of the sensing task; task duration refers to the expected runtime of the sensing task; and time budget (T_budget) refers to the upper limit of end-to-end latency from when the sensing task is initiated by the AF to when the sensing result is obtained. Specifically, time constraints can be used to determine scheduling decisions, whether local or globally coordinated.

[0062] S202: The second core network element determines the first core network element to perform the perception task.

[0063] In this embodiment, after obtaining a sensing task, the Core-SCF can select a suitable first core network element (i.e., Edge-SCF) for the sensing task so that the first core network element can handle the sensing task.

[0064] Specifically, you can select a suitable Edge-SCF in the following ways: (I) Edge-SCF Information Registration and Discovery Mechanism In this embodiment, Edge-SCF's information registration is not only compatible with the network repository function (NRF) mechanism in the 3GPP standard, but can also be directly registered to Core-SCF's optimization options to improve efficiency. The NRF mechanism is responsible for network function registration, discovery, and state management in the 5G service-oriented architecture.

[0065] The choice of Edge-SCF depends on the deployment scale and latency requirements: 1. Standard NRF-based mechanism (recommended default): As a distributed network function (NF), Edge-SCF registers its service profile with the NRF upon startup or configuration changes. This profile includes: NF type (Edge-SCF), service area, supported capabilities (QoS range, mode set), load threshold, and heartbeat interval. The mode set refers to the set of awareness modes that the nodes managed by the Edge-SCF can support, or the set of awareness modes that the Edge-SCF can coordinate. Edge-SCF monitors its own load metrics (such as CPU utilization, memory usage, and concurrent tasks). The load threshold is used to quickly filter and protect edge nodes, preventing new awareness tasks from being assigned to resource-scarce or overloaded Edge-SCFs. The heartbeat interval is a health check and keep-alive mechanism that defines the period at which the Edge-SCF periodically sends a "heartbeat" signal (a simple status notification message) to the NRF (or Core-SCF). Every "heartbeat interval" (e.g., 30 seconds), Edge-SCF will automatically send a heartbeat message to NRF to indicate that it is "still running normally". NRF will record the time when each heartbeat is received.

[0066] 2. Optimization mechanism for direct registration to Core-SCF (optional high-efficiency mode): Edge-SCF registers directly with Core-SCF (through a dedicated interface or an extended NRF service), reporting a brief summary: service region, capability vector, and load threshold. Core-SCF maintains an internal lightweight database (Hot-Edge list), updated periodically (heartbeat interval every 30-60 seconds). When selecting an Edge-SCF, Core-SCF evaluates it directly from its local database, without requiring NRF queries.

[0067] This registration mechanism can eliminate NRF query latency (saving 5-10ms), making it suitable for small-scale or latency-sensitive deployments (e.g., <50 Edge-SCFs). However, Core-SCF needs to handle the registration load, which may become a bottleneck if the scale is large; it can be combined with a time-to-live (TTL) mechanism to prevent expired registrations from becoming invalid.

[0068] In practical implementation, the direct registration mechanism can be used as a deployment option. For high-priority ISAC-aware tasks (such as those with a latency of <100ms), direct registration can be enabled; otherwise, NRF should be used to comply with protocol requirements.

[0069] (ii) Upon receiving a perception service request, Core-SCF selects the appropriate Edge-SCF based on the following three types of information: Step 1: Preliminary filtering based on NRF (hard conditions) (1) After receiving the sensing task, Core-SCF queries the NRF to specify the filtering conditions. The filtering conditions include: service area matches target area, NF status = AVAILABLE, and supported service type = ISAC_CONTROL.

[0070] (2) NRF returns a list of candidate Edge-SCFs, which contains Edge-SCFs that meet the above filtering conditions.

[0071] Step 2: Detailed assessment based on multiple dimensions (soft conditions) For each Edge-SCF in the list, Core-SCF can be quantitatively evaluated from one or more of the following dimensions: (1) Geographic location and coverage matching Target area matching: When Core-SCF receives a perception task containing a target area (such as latitude and longitude range, cell ID), it will quickly query the NRF or local (depending on the deployment) to locate the primary responsible Edge-SCF (the target area is completely within its coverage area) and the backup Edge-SCF (partial coverage or adjacent coverage).

[0072] Specifically, the overlap or center point distance between the target area and the "service area" registered with the Edge-SCF is calculated. The larger the overlap area or the closer the distance, the higher the score.

[0073] (2) Capacity and load status Capability Registration: When Edge-SCF registers with Core-SCF or NRF (depending on the deployment), it reports its capability summary, including: the maximum number of concurrent tasks supported; the supported perception modes; the computing capability level (e.g., lightweight / standard); whether it supports specific types of perception entities (e.g., drones, vehicle-mounted units); and the real-time load.

[0074] Edge-SCF is required to report its real-time load metrics to Core-SCF or NRF (depending on the deployment), including: CPU / memory utilization; current number of concurrent tasks; cache hit rate; and average round-trip latency to the control plane / user plane.

[0075] Specifically, Capability Matching (S_capability): This assesses the degree of match between the "supported capabilities" registered by the Edge-SCF and the requirements of the sensing task. For example, if the task requires "Mode 2," then an Edge-SCF that supports Mode 2 will score highly in this area; if the task requires "high precision," then an Edge-SCF that supports high precision within its QoS range will score highly. Real-time Load (S_load): This is calculated based on the load metrics reported by the Edge-SCF in real time; the lower the load, the higher the score.

[0076] (3) Historical performance and reputation Task completion statistics: Core-SCF records the historical task completion status of each Edge-SCF, including: task success rate; average response latency; cross-domain cooperation level; and dynamic reputation score. The dynamic reputation score is an internal reputation score calculated for each Edge-SCF based on historical data. When multiple candidate Edge-SCFs overlap, those with higher reputation scores are prioritized. This reputation score can be determined based on one or more of the following: historical task success rate, average response latency (shorter is better), task anomaly rate, and cross-domain cooperation level (such as the proportion of cross-domain task scheduling accepted).

[0077] S203: The second core network element sends a sensing task to the first core network element, and the first core network element receives the sensing task accordingly.

[0078] S204: The first core network element determines the perception strategy based on the quality of service requirements.

[0079] The perception strategy can be one of selecting only the perception mode for the perception task, selecting only the perception node for the perception task, or selecting both the perception mode and the perception node for the perception task.

[0080] Among these options, selecting only the sensing mode is primarily applicable to scenarios where sensing entity resources are limited but their functions are flexible. When there is only one or a few sensing entities within the local area corresponding to the first core network element, and the target is still within its coverage area but service quality deteriorates, since there are no other alternative sensing entities, the only way to maintain service is to switch sensing modes on the current sensing entity. For example, if the signal quality degradation is due to the propagation environment (such as obstruction or multipath) rather than a coverage problem of the sensing entity itself, switching sensing modes (such as switching the roles of the transmitter and receiver) can effectively bypass obstruction or improve the robustness of the link. Therefore, when a decrease in positioning accuracy or communication rate is detected, a sufficiently flexible sensing entity can find the optimal balance point between different sensing modes to optimize performance by switching sensing modes.

[0081] Selecting only sensing nodes: The core of this strategy is to change the sensing entity while maintaining the current working mode (sensing mode). It is typically triggered by the movement of the sensing entity or its resource status. When the target movement causes insufficient coverage by the current sensing entity, and a newly entered sensing entity supports the same mode and meets service quality requirements, switching sensing entities is the optimal choice. Furthermore, when the current sensing entity is resource-constrained (e.g., insufficient PRB, insufficient computing power) or the link quality deteriorates due to inherent conditions such as the sensing entity's location, if other sensing entities in the same area can provide better links or resources with the same sensing mode, then directly replacing the sensing entity while maintaining the same sensing mode is the most efficient solution to ensure key performance indicators (e.g., latency, bandwidth, positioning accuracy). This strategy is also suitable for scenarios where sensing entities have limited capabilities and only support a few sensing modes.

[0082] In this context, the resource status of a sensing entity refers to the real-time availability of its computing, communication, and energy resources. In this embodiment, the resource status refers to a quantifiable indicator reflecting the "resource score R." When these indicators deteriorate to a certain extent, a sensing node switch is triggered. Specifically, the resource status includes energy status (finite or infinite), computing power, and resource occupancy status. For battery-powered devices, energy status refers to the remaining percentage of battery power or the estimated continuous operating time; computing power refers to the current computing load or available computing power; and resource occupancy status refers to the remaining percentage of the sensing-dedicated bandwidth (BWP).

[0083] Simultaneous selection of sensing nodes and sensing modes: This is a complex strategy that requires changing the operating mode of a sensing entity while replacing it. It typically occurs when there are significant changes in network topology or task requirements. For example, if the current sensing entity is about to fail or has insufficient coverage, and the sensing modes supported by other available sensing entities in the area do not match the requirements of the current sensing task, then it is necessary to simultaneously select a new sensing entity and enable a new sensing mode on that entity.

[0084] Another typical situation is that the functional support of sensing entities is asymmetrical, that is, different sensing entities support different sensing modes; when the target moves into the coverage area of ​​a new sensing entity, the original sensing mode may no longer be usable, and the switching of sensing entity and sensing mode must be performed simultaneously.

[0085] Specifically, the perception strategy that matches the perception task can be determined in the following ways: (1) The first core network element determines the weight of each parameter in the first parameter set according to the service quality requirements, and obtains the first weight set.

[0086] The first parameter set includes at least one parameter, which reflects the capabilities of the first sensing node. The first sensing node can be any sensing node in the first sensing node set. The first sensing node set is the set of sensing nodes in the local area corresponding to the first core network element. Specifically, the first parameter set includes one or more of the following: geometric score G, signal quality score Q, sensing performance score P, resource score R, and reputation score T. That is, one sensing node corresponds to one parameter set.

[0087] Among them, the geometric score G indicates the spatial relationship between the sensing node and the target, and / or the spatial relationship between the sensing node and other sensing nodes; the signal quality score Q indicates the communication quality of the wireless link between the sensing node and the target; the sensing performance score P indicates the potential accuracy and capability of the sensing entity in performing sensing tasks; the resource score P indicates the sufficiency of available resources in the sensing node; and the reputation score T indicates the performance and reliability of the sensing node when performing historical sensing tasks. The specific calculation methods for the above parameters will be explained in subsequent embodiments.

[0088] In some implementations, the first weight set can be obtained as follows: The first core network element obtains a first vector based on service quality requirements, the first vector including vectors corresponding to each indicator in the service quality requirements; the first core network element determines the first weight set based on the correlation matrix and the first vector. Here, the correlation matrix refers to the correlation matrix between the service quality requirements and the first parameter set. The indicators included in the service quality requirements may include accuracy, latency, reliability, and resource consumption, etc. That is, the first core network element calculates the weights corresponding to different capabilities based on the constraints of the sensing task on indicators such as accuracy, latency, reliability, and resource consumption, so as to select an appropriate sensing strategy for the sensing task based on these weights, thereby realizing a QoS-driven adaptive selection mechanism.

[0089] Specifically, it can be determined in the following ways: ①The QoS requirement vector corresponding to the perception task is: ; in, Indicates the required perception accuracy (such as target positioning accuracy, resolution); Indicates latency sensitivity (the higher the value, the lower the latency requirement); Indicates reliability requirements (continuity, robustness, security); This indicates resource sensitivity (the degree of concern regarding resource usage / energy consumption). It should be noted that all of the above can be normalized to... A higher value indicates a higher demand.

[0090] ②The weight vector is defined as: ; ③ The scoring dimensions corresponding to the candidate perceived entities are: ; ④ Define a QoS-scoring dimension correlation matrix M:

[0091] in, This represents the degree of influence of the j-th type of QoS on the i-th scoring dimension, and is a predefined or learned non-negative coefficient.

[0092] ⑤ Weight calculation formula:

[0093] ⑥ Normalization yields the final weights: Method 1:

[0094] Method 2: Temperature-controlled softmax reduces the dominance of a single QoS:

[0095] in, Control of concentration ( default, (Magnify the differences).

[0096] As can be seen, the weights corresponding to different parameters in the first parameter set are not fixed, but dynamically determined according to the service quality requirements of the perception task. For example, referring to the QoS-driven weight mapping table in Table 1, the weight adjustment strategy, typical values, and applicable service types corresponding to different QoS levels can be determined through this mapping table.

[0097] Table 1 QoS-driven weight mapping

[0098] (2) The first core network element obtains a score of the perception strategy based on at least some of the parameters in the first parameter set and at least some of the weights in the first weight set.

[0099] (3) The first core network element determines the perception strategy that matches the perception task based on the score of the perception strategy.

[0100] After obtaining the first weight set, the first core network element obtains a score corresponding to each perception strategy based on at least some parameters in the first parameter set and at least some weights in the first weight set. Then, based on the score corresponding to each perception strategy, the most suitable perception strategy for the perception task is determined. The parameters in the first parameter set correspond one-to-one with the weights in the first weight set.

[0101] Specifically, the perception strategy that matches the perception task can be determined in the following ways: The first core network element first obtains a score for the first perception strategy, and then determines whether the score of the first perception strategy meets the fifth preset condition. If so, it determines that the first perception strategy matches the perception task. The first perception strategy indicates the selection of a perception mode for the perception task.

[0102] If the score of the first perception strategy does not meet the fifth preset condition, the first core network element obtains the score of the second perception strategy and determines whether the score of the second perception strategy meets the sixth preset condition. If it does, it determines that the second perception strategy matches the perception task.

[0103] If the score of the second perception strategy does not meet the sixth preset condition, the first core network element obtains the score of the third perception strategy and determines whether the score of the third perception strategy meets the seventh preset condition. If it does, the third perception strategy is determined to match the perception task.

[0104] That is, a simple strategy is prioritized (selecting only the sensing mode or only the sensing entity) to minimize switching overhead. If the threshold is not met, the strategy is upgraded to joint (selecting both the sensing mode and the sensing entity). The preset conditions can be configured according to the actual application situation, and the specific performance will be described in subsequent embodiments.

[0105] Specifically, based on score comparisons, a perceptual strategy is selected: (1) Input preparation:

[0106] Extract QoS requirements (acc_req precision, delay_req latency, etc.) from service requests and generate a weighted mapping vector. .

[0107] Retrieve the current set of perceived entities N (|N| is the number) and the set of patterns M (|M| is the number) from the capability database.

[0108] Initialize real-time scoring: for each entity n j ∈N and pattern m i ∈M Calculation of Dimensional Scores j represents the j-th perceptual entity, and i represents the i-th perceptual mode. This means that when the perceived entity n j Using the perception mode m i When working, it scores on five competency dimensions.

[0109] Then based on dimensional scores and weight Calculate the comprehensive score corresponding to each perception strategy. .

[0110] The score for the first perception strategy is: The score for the second perception strategy is The third perception strategy was rated as follows: . It assesses the overall potential of the strategy of "switching sensing modes on the current fixed set of sensing nodes," not the score of a specific sensing mode. Specifically, This could be the maximum or average score of all current perception modes. Similarly, It assesses the overall potential of the strategy of "switching sensing nodes within the current fixed sensing mode," not the score of a single sensing node. Specifically, It can be the maximum score of all sensing nodes in this sensing mode. It assesses the overall potential of the strategy of "simultaneously changing sensing nodes and sensing modes," and is a forecast or a score prediction of the optimal combination.

[0111] For the implementation of obtaining the score of a sensing node under a fixed sensing mode, please refer to the subsequent calculation of Snodej; for the implementation of obtaining the score of a sensing mode under a sensing node, please refer to the subsequent calculation of Smodei.

[0112] (2) Output the most efficient strategy. Based on the obtained comprehensive score, determine the optimal perception strategy using judgment rules. The judgment rules can be if-else chains, combined with thresholds to ensure robustness. If there is no solution, back off (e.g., downgrade QoS or notify the application layer).

[0113] It should be noted that before selecting a perception strategy based on scores, a screening process can be performed based on pre-configured conditions to determine which perception strategies are feasible, and then the optimal perception strategy can be determined based on scores.

[0114] The selection criterion for the first sensing strategy (selecting only sensing mode) is to determine whether the set of sensing nodes (sensing entities) available for the current sensing task is basically fixed or very limited in number (e.g., the target is in an indoor environment with only a single base station). If "yes", the strategy selection is constrained to only improve on existing nodes, i.e., "selecting only sensing mode" is given priority. This is because the option of replacing sensing nodes is either nonexistent or extremely costly.

[0115] The selection criterion for the second sensing strategy (selecting only sensing nodes) is to determine whether the current sensing task must or strongly requires the use of a certain sensing mode (for example, the task of sensing must use "mode 3: base station transmits and terminal receives"). If "yes", the strategy selection is constrained to be carried out while keeping the mode unchanged, and "selecting only sensing nodes" is given priority.

[0116] The first core network element matches the sensing strategy for the sensing task under the premise that a triggering event has occurred and / or the resource status of the sensing node has changed. For example, the target moves, causing insufficient coverage of the original node (triggering "select only node"); the current sensing node's resources are exhausted (low power, high load) (triggering "select only node"); the wireless environment deteriorates (such as poor signal due to obstruction), but the sensing node's location remains unchanged (triggering "select only mode").

[0117] ① Filtering the perception-only mode selection strategy (fixed N, optimized M): Judgment rule: Applicable to scenarios with fixed entities or limited resources (small |N|, flexible mode). If entity coverage is stable but QoS degradation is caused by the environment (e.g., multipath), and switching the perception mode significantly improves the score, then the perception mode should be switched first.

[0118] Triggering example: Coverage decreases due to occlusion of a single gNB.

[0119] Decision Formula (1):

[0120] in, This indicates the number of perceived entities in the current set of perceived entities. This indicates a threshold for the size of the sensed entities. If the number of entities in a set of sensed entities is less than this threshold, it can be considered fixed. For example... =3; (Average resource points) Indicates the resource availability threshold, for example (Low resource threshold); (Average perception mode performance) This parameter represents the threshold of the pattern's performance potential. It can be used to determine whether there are perception patterns with sufficiently high performance potential, for example... (High-performance threshold); This parameter represents the policy payoff gap threshold. It defines how much higher the expected maximum score of the "select only perception mode" policy needs to be compared to competing policies to justify its selection. For example... =0.2.

[0121] When the score of the first perception strategy satisfies the decision formula (1) above, the first core network element selects the first perception strategy.

[0122] ② Filtering Entity-Aware Selection Strategy (Fixed M, Optimized N): Judgment rule: Applicable to scenarios with fixed sensing patterns or diverse entities (small |M|, large entity changes). If QoS degradation originates from entity movement / load, and the alternative sensing entity has a higher score, the sensing entity will be switched first.

[0123] Triggering example: Target movement causes insufficient coverage for the current UE, switch to a neighboring RSU that supports the same mode to improve G / R.

[0124] Decision Formula (2):

[0125] in, This represents the maximum threshold for supported perception modes, for example... ; (Geometric variance) Indicates a high geometric diversity threshold, for example =0.3; The threshold representing the score difference between different perceived entities, for example =0.15. Among them, candidate sensing entities With the sensing entity currently performing the task The difference in overall score between the two values ​​under a fixed perception mode.

[0126] When the second perception strategy satisfies the above decision formula (2) in terms of score, the first core network element selects the second perception strategy.

[0127] ③ Select strategies simultaneously by filtering perceived entities and patterns (unconstrained, joint optimization of M and N): Judgment rules: Applicable to complex and changing scenarios (without fixed constraints). If a single strategy score is insufficient, but the combined score is significantly better, then that strategy is prioritized. A two-stage coarse screening (traversing M to select Top-M') + fine screening (screening N on m_opt) is used. Thresholds: φ_delta = 0.1 (joint advantage threshold), M' = 3 (size of the coarse screening subset).

[0128] Triggering example: Cross-domain tasks such as drone formations require joint switching due to mode / entity mismatch.

[0129] Decision Formula (3):

[0130] in, For the rate of change, Indicates the threshold of rate of change; This represents the joint advantage threshold.

[0131] Here, the rate of change can refer to the rate of change of position, for example, the rate of change of position = Δpos / t> =30% / s. Specifically, the rate of change can also be the rate of change of wireless link quality, such as the rate of decrease in signal strength and / or signal-to-noise ratio; the rate of change of the resource status of sensing nodes, such as the rate of decrease in the remaining power of sensing nodes and the rate of increase in computing load; the rate of change of network topology, such as the change in the number of sensing nodes per unit time; the rate of change of QoS requirements for sensing tasks, such as the tightening rate of requirements for sensing accuracy, maximum latency, etc.; and the rate of degradation of sensing performance, such as the rate of increase in positioning error, etc.

[0132] Explanation of each judgment indicator in the above decision formula: in, (Sufficient node resources) indicates that the number of available sensing nodes must be large enough.

[0133] This condition ensures sufficient node resources for combination and optimization. If there are too few nodes, joint selection becomes meaningless, and the "select only perception mode" strategy should be prioritized.

[0134] The condition of "sufficient pattern resources" indicates that the number of available sensing patterns must be sufficiently large. This condition ensures a rich selection of patterns. If there are too few patterns, joint selection becomes meaningless, and a "select only sensing nodes" strategy should be prioritized.

[0135] > (Environmental upheaval trigger condition) indicates that the network or task environment is undergoing rapid and drastic changes, and the rate of change exceeds a preset stability threshold. This judgment is the direct cause of the joint selection. When simple strategies (adjusting only points or only modes) may fail to keep up with environmental changes, more complex joint optimization is needed to address the issue.

[0136] The "significant benefit condition" indicates that the highest expected performance score achievable by the "joint selection" strategy must be significantly higher than the highest expected scores of the two simpler strategies: "mode-only selection" and "node-only selection." This judgment is a cost-benefit assessment. Even with drastic environmental changes, if the performance gain from the joint strategy is insufficient (not exceeding the threshold φ_delta), the additional coordination overhead and complexity are not worthwhile. This complex mechanism is only worthwhile when the benefit is sufficiently significant.

[0137] It should be noted that when the first core network element determines the perception strategy for the perception task, decision formula (1) is used first, followed by decision formula (2), and finally decision formula (3).

[0138] S205: The first core network element determines the sensing node and / or sensing mode that matches the sensing task according to the sensing strategy.

[0139] After determining the optimal perception strategy, the optimal perception entity, perception mode, or "perception entity-perception mode" combination is selected for the perception task based on the perception strategy to meet the service quality requirements of the perception task in the most efficient and reliable way.

[0140] Specifically, S205 can be implemented as follows: (a) Select only the perception mode If the perception strategy indicates that a perception mode should be selected for the perception task, the first core network element obtains the first score corresponding to each perception mode under the first set of perception nodes; the first core network element determines the perception mode that satisfies the first preset condition as the perception mode that matches the perception task. For example, the first preset condition can be the perception mode with the highest score.

[0141] Specifically, the first core network element obtains the first score corresponding to each sensing mode under the first sensing node set, including: the first core network element determines the weight corresponding to each parameter in the first parameter set according to the service quality requirements, and obtains the first weight set; the first core network element determines the first score corresponding to a sensing mode based on at least some parameters in the first parameter set and at least some weights in the first weight set. The acquisition of the first parameter set and the first weight set can be found in the foregoing.

[0142] Given a fixed set of sensing entities performing the sensing task, the most efficient operating mode is selected for this set of sensing entities. Specifically, the first core network element obtains the first score corresponding to each sensing mode; the first core network element determines the sensing mode with the highest first score as the sensing mode that matches the sensing task.

[0143] The first score corresponding to each perception mode can be calculated using the following formula:

[0144] in, The score is the score corresponding to the i-th pattern. This represents the weight corresponding to the perceived performance score. This indicates the weight corresponding to the resource score. The scores represent the signal quality. P is the sensing performance score, determined based on the theoretical accuracy and range of sensing mode i; R is the resource efficiency score, based on the average resource consumption of sensing mode i on the sensing entity set N; and Q is the signal quality score, based on the expected signal quality of sensing mode i on the sensing entity set N.

[0145] By iterating through each perception mode, a score is obtained for each perception mode, and the perception mode with the highest score is selected.

[0146] The impact of different perception modes on the specific values ​​of P, Q, and R includes: (1) Self-transmitting and self-receiving mode (single-station radar mode) The impact on P is moderate in accuracy and short in range; due to the co-location of transmission and reception, the geometric baseline is zero, resulting in low angle measurement accuracy; however, signal processing is simple, and distance resolution is fundamentally guaranteed; the value of P is moderate.

[0147] Impact on R: Low resource consumption; only a single entity needs to work, occupying its own PRB and computing power, with no collaboration overhead; R has a relatively high value.

[0148] Impact on Q: High requirements for Q; relies on strong reflection signals from a single link, requiring extremely high signal-to-noise ratio (SINR) for reliable detection; Q has a high weight.

[0149] (2) One transmit, multiple receive (multiple base station reception) The impact on P is as follows: high accuracy (especially angle measurement); the use of multiple receiving stations to form a long baseline results in a superior geometric configuration, significantly improving the accuracy of AoA estimation; and P has a high value.

[0150] The impact on R is as follows: moderate resource consumption; one transmitting entity and multiple receiving entities work together, consuming multiple sets of receiving processing resources and backhaul bandwidth; the value of R is moderate.

[0151] The impact on Q is as follows: the requirement for Q is moderate; the receiver diversity gain improves robustness and reduces the requirement for the extreme SNR of a single link; the stability of Q is more important.

[0152] (3) Downlink cooperation (base station transmits, terminal receives) The impact on P is as follows: accuracy and range depend on terminal distribution; using widely distributed terminals may result in excellent geometry. However, the capabilities of terminals vary, so the value of P may be very high but fluctuates greatly.

[0153] The impact on R is: high resource consumption. It involves a large number of terminal devices, requiring the scheduling of their air interface and computing resources, resulting in high signaling and coordination overhead, and thus a lower value for R.

[0154] The impact on Q is as follows: Q requirements are complex. It requires evaluating the downlink quality from the base station to multiple terminals, focusing more on coverage uniformity rather than single-point peaks. Q-score calculation is more comprehensive.

[0155] (4) Uplink collaboration (e.g., terminal equipment transmits, base station receives) The impact on P is similar to that of Mode 3, where the accuracy depends on the distribution of the terminal (transmitter), and the value characteristics of P are similar to those of Mode 3.

[0156] The impact on R is: high resource consumption. It requires scheduling uplink transmission resources from multiple terminal devices, as well as multi-stream data processing resources on the base station side, resulting in a lower R score.

[0157] The impact on Q is as follows: Q requirements are complex, requiring the evaluation of uplink quality from multiple terminals to the base station.

[0158] (5) Test and traverse collaboration (direct communication between terminals) The impact on P is: high accuracy (relative relationship). It is suitable for high-precision relative sensing within dense terminal groups, and the value of P is high in specific scenarios.

[0159] The impact on R is: Medium resource consumption. Using direct link (SL) resources does not consume cellular network access point resources, but requires synchronization between terminals. R score is moderate.

[0160] The impact on Q is as follows: High requirements are placed on Q. Direct links between terminals are susceptible to obstruction and interference, necessitating the maintenance of high-quality sidelinks. Therefore, the value of Q is extremely critical.

[0161] (6) Hybrid / network-assisted (a complex strategy or scheme that uses multiple sensing modes simultaneously or in a time-sharing manner for collaborative sensing) The impact on P is: highest potential for accuracy. It integrates multiple transmission and reception paths, resulting in the richest information dimensions. Theoretically, P can take the highest value.

[0162] The impact on R is: highest resource consumption. Coordinating multiple types of entities and multiple links leads to an explosive increase in scheduling and computational complexity. The value of R is very low.

[0163] The impact on Q is as follows: Q requires a comprehensive assessment. The overall quality of heterogeneous links (cellular + pass-through) needs to be evaluated. The evaluation of Q's value is the most complex.

[0164] It is evident that the perception mode is crucial in determining the baseline for P, R, and Q scores. When calculating the score of a perceived entity under a given perception mode: P: Directly related to the theoretical accuracy and upper limit of the range of the model.

[0165] R: Calculate the expected resource consumption required for this entity to run this mode (compared to its currently available resources).

[0166] Q: Evaluate the link quality corresponding to the specific role (sender or receiver) that the entity plays in this mode.

[0167] It should be noted that when the same perception pattern is executed by different perception entities, the corresponding (P, Q, R) will be different. Therefore, the scoring needs to be accurate to the granularity of "entity-pattern" pair.

[0168] (ii) Select only sensing nodes If the perception strategy indicates that a perception node is selected for the perception task, the first core network element obtains the second score corresponding to the first perception node under the target perception mode. The first core network element determines the first perception node whose second score meets the second preset condition as the perception node matching the perception task. Here, the first perception node is any one of the first perception nodes in the set of first perception nodes, and the target perception mode indicates the perception mode currently supported by the set of first perception nodes. The second preset condition can be set according to actual needs; for example, the second preset condition can be the highest score or a score greater than a preset threshold.

[0169] Specifically, the first core network element obtains the second score corresponding to the first sensing node in the target sensing mode, including: the first core network element determines the weight corresponding to each parameter in the first parameter set according to the service quality requirements, and obtains a first weight set; the first core network element determines the second score corresponding to the first sensing node based on at least some parameters in the first parameter set and at least some weights in the first weight set. The acquisition of the first parameter set and the first weight set can be found in the foregoing content.

[0170] Specifically, given a fixed perception pattern, the most suitable perception entity for executing that pattern is selected. Specifically, the first core network element obtains the second score corresponding to the first perception node; the first core network element determines the first perception node whose second score meets a first preset condition as the perception node matching the perception task. Here, the first perception node can be any one of the first perception nodes in the set of first perception nodes.

[0171] In practical implementation, the second score corresponding to the first sensing node can be calculated using the following formula:

[0172] in, The second score corresponds to the j-th perception node. Representing geometric fractions The corresponding weights Indicates signal quality score The corresponding weights Represents resource score The corresponding weights Represents credit score The corresponding weights.

[0173] After obtaining the second score for each sensing node, the sensing entity with the highest Snodej score (such as the primary emitting sensing entity) or the Top-K sensing entities (such as the cooperative sensing set) are selected as the final choice.

[0174] The impact of different perceived entity types on G, Q, R, and T can be summarized as follows: (1) Macro base station Impact on G: The location is fixed and known, and the altitude is high. A long geometric baseline formed with the target and other sensed entities is beneficial for improving the geometric dilution of precision (GDOP), resulting in a typically high and stable G score. GDOP quantifies the relative position, orientation, and topological advantages of a sensed entity with respect to the target.

[0175] Impact on Q: High transmit power and powerful antenna array; providing high and stable reference signal received power (RSRP) and / or signal to interference plus noise ratio (SINR), Q score is usually very high.

[0176] Impact on R: Resources are plentiful (power, computing, PRB), but the load may be heavy. R score is usually high, but fluctuates with load.

[0177] Impact on T: Long service history, stable and reliable, T score is usually very high.

[0178] (2) Micro base station or small station Impact on G: Fixed location, but potentially dense deployment. Short baseline, but diverse geometries can be achieved through dense deployment. Moderate G score, but good diversity.

[0179] Impact on Q: Small coverage area and low link budget. High Q score over short distances, but it decays rapidly with distance. In other words, the Q score is high but unstable.

[0180] Impact on R: With moderate resources and relatively light load, the R score is usually high.

[0181] Impact on T: High stability, but historical data may be limited, T score is moderately high.

[0182] (3) Vehicle-mounted terminal Impact on G: The location continues to move at high speed. It can provide a unique geometric perspective that changes dynamically, potentially greatly improving the overall GDOP. G has high potential, but changes extremely rapidly.

[0183] Impact on Q: Low transmit power and simple antenna. Q score is greatly affected by vehicle body obstruction and high-speed moving Doppler. That is, Q score is low and fluctuates drastically.

[0184] Impact on R: Limited energy (batteries) and limited computing power. That is, the R score is usually low and is the main constraint.

[0185] Impact on T: High mobility, random participation in tasks, sparse historical success rate data, low T score or initial value.

[0186] (4) Handheld terminal Impact on G: Positional movement, but relatively slow; geometric perspective is beneficial but changes are slow; G score is moderate. Impact on Q: Low power results in mediocre antenna performance, significant handheld operation impacts Q score, and results in low to medium Q score.

[0187] Impact on R: Energy is severely limited, computing power is average, and R score is very low.

[0188] Impact on T: Participation willingness / ability is uncertain, T score is very low.

[0189] It is evident that the sensing entity is the main carrier of G, Q, R, and T scores, and their differences are directly reflected in the following: G is determined by the physical location and mobility of the sensing entity; Q is determined by the radio frequency hardware capabilities of the sensing entity and the real-time wireless environment; R is determined by the sensing entity's equipment platform (energy, computing power) and current task load; and T is determined by the sensing entity's performance record in historical tasks.

[0190] (iii) Simultaneously select sensing nodes and sensing modes Specifically, the first core network element acquires a third score corresponding to the second sensing node under the first sensing mode; the first core network element determines the second sensing node whose third score under the first sensing mode meets a third preset condition; the first core network element acquires a fourth score corresponding to the first sensing combination, which includes the first sensing mode and a set of second sensing nodes, the set of second sensing nodes including second sensing nodes that meet the third preset condition; the first core network element determines the first sensing combination whose fourth score meets the fourth preset condition as the sensing node and sensing mode matching the sensing task. The acquisition of the third score can be referred to the acquisition of the second score described above, and the third and fourth preset conditions can be configured according to the actual application scenario.

[0191] Specifically, the first core network element obtains a fourth score corresponding to the first sensing combination, including: the first core network element summing the third scores corresponding to each second sensing node in the second sensing node set to obtain a first calculation result; the first core network element obtaining a second calculation result based on the target indicators corresponding to each second sensing node in the second sensing node set; and the first core network element using the difference between the first calculation result and the second calculation result as the fourth score corresponding to the first sensing combination. The target indicator is used to indicate the cost required to jointly use the first sensing mode and the second sensing nodes.

[0192] In the specific implementation, firstly, all possible sets of perception modes M and sets of perception entities N are input. Secondly, a perception mode from the set of perception modes M is selected, and the perception task is re-analyzed and weighted to obtain a weight mapping table for G, Q, P, R, and T. Then, Edge-SCF directly retrieves the perception entities supporting this perception mode and their corresponding perception capabilities from its own perception entity cache. Next, based on this perception mode, a comprehensive score is calculated for the perception entity set. Finally, the perception mode and perception entities with the highest comprehensive score (Top-K) are selected, and the reselected perception entities are configured.

[0193] Decision and output: Among all (mi, Nj) combinations, select the combination (mopt, Nopt) with the highest Sjoint score as the global optimal solution.

[0194] For example, first fix the perception pattern. In the complete set of nodes N, apply the "perceived entity selection only" strategy (formula Snodej) to calculate the applicability score of each perceiving node under this perception pattern. Select the Top-K nodes with the highest scores under this perception pattern to form a candidate node subset Nk. Calculate the joint utility score Sjointk of this "perceived pattern-node subset" combination (mk, Nk). Then, select the optimal combination based on the joint utility score of each combination.

[0195] The joint utility score S_joint_i can be calculated using the following formula:

[0196] Among them, Cost (target metric) represents penalties such as coordination overhead and total resource consumption. The penalty weighting coefficient is used to balance performance and cost. The core components and calculation method of the penalty term (Cost) are as follows:

[0197] in, These are the weighting coefficients for each cost dimension, used to adjust the importance of different costs.

[0198] ① Coordination complexity cost

[0199] This cost reflects the control plane signaling overhead and management complexity required to establish and maintain this collaborative combination.

[0200] Key influencing factors: the number of sensing entities involved in the collaboration, cross-domain situations, and the coordination difficulty of the model itself.

[0201] Calculation formula:

[0202] The number of sensing entities within the set. The larger the number, the more scheduling signals are generated.

[0203] Indicator function, if If a node belongs to multiple Edge-SCF domains, the value is 1; otherwise, it is 0. Cross-domain collaboration introduces additional core network signaling.

[0204] : The inherent coordination coefficient of the mode. For example, mode 1 (spontaneous and self-receiving) has a coefficient of 0.1, and mode 6 (hybrid collaboration) has a coefficient of 1.0.

[0205] Calibration coefficient.

[0206] ② Total resource consumption cost

[0207] This cost exceeds the "R-points" of a single node, assessing the overall network resource utilization of this combination from a system-wide perspective.

[0208] Key influencing factors: total energy consumption, total PRB usage, and total backhaul bandwidth requirements.

[0209] Calculation formula:

[0210] : Estimated nodes Execution mode The required energy consumption, air interface resources, and backhaul bandwidth.

[0211] The denominator is the total availability of corresponding resources within the Edge-SCF domain (or globally across domains). The higher the ratio, the greater the cost.

[0212] ③ Cost of spatiotemporal synchronization accuracy

[0213] This cost assessment considers the potential performance loss to sensing performance (especially for coherent processing-based modes) caused by inter-node time synchronization errors and location uncertainties. Key influencing factors include: node type (high base station synchronization accuracy, low terminal synchronization accuracy), clock source differences between nodes, and geographic location accuracy.

[0214] Calculation formula:

[0215] The average time synchronization error between nodes within the set (e.g., nanosecond level).

[0216] : The sensitivity coefficient of the mode to synchronization errors. For example, modes that rely on coherent synthesis have high sensitivity (set to 1.0), while incoherent modes have low sensitivity (set to 0.2).

[0217] ④ Network topology risk costs

[0218] This cost assessment considers the risk of mission disruption due to node mobility or wireless link vulnerability in the selected combination.

[0219] Key influencing factors: the proportion of mobile nodes, predicted link stability, and variance of node "reputation score (T)".

[0220] Calculation formula:

[0221] The higher the proportion of mobile nodes and the lower the score of the least stable node, the higher the overall risk cost.

[0222] The above embodiments illustrate how to select appropriate sensing modes and / or sensing nodes for a sensing task based on its quality of service requirements. Specifically, when performing the selection operation, the first core network element will consider the capabilities reported by each sensing entity, such as geometric score G, signal quality score Q, sensing performance score P, resource score R, and reputation score T. The following will describe, with specific embodiments, how sensing entities report their capabilities and the specific calculation methods for different capability items.

[0223] See Figure 3 This diagram is an interactive flow diagram of a perception entity capability subscription process provided in an embodiment of this application, such as... Figure 3 As shown, the method includes: S301: Application Function (AF) initiates a perception task to Core-SCF via Network Open Function (NEF).

[0224] The perception task can include task type, service quality requirements, and time constraint information.

[0225] S302: Core-SCF selects the appropriate Edge-SCF for perception tasks.

[0226] For details on the specific implementation of Core-SCF selecting a suitable Edge-SCF, please refer to the relevant descriptions in the above embodiments.

[0227] S303: Core-SCF sends perception tasks to Edge-SCF.

[0228] S304: Edge-SCF sends a sensing capability subscription request to the sensing entity gNB.

[0229] S305: Edge-SCF sends a sensing capability subscription request to the sensing entity UE.

[0230] In this embodiment, after receiving a sensing task, Edge-SCF begins subscribing to sensing capability information from sensing entities (including gNB and UE). Specifically, Edge-SCF sends a "sensing capability subscription request" to both gNB and UE. This request includes a list of capabilities to be reported, update mechanisms (such as event-triggered or periodic reporting), and relevant information about the sensing targets.

[0231] S306: The sensing entity gNB agrees to subscribe and performs measurements on the sensing target.

[0232] S307: The sensing entity UE agrees to subscribe and performs measurements on the sensing target.

[0233] The results of measurements on the sensing target can include RSRP, SINR, and channel quality indicator (CQI), which represent the current channel quality.

[0234] S308: The sensing entity gNB reports its sensing capabilities to the Edge-SCF.

[0235] S309: The sensing entity UE reports its sensing capabilities to the Edge-SCF.

[0236] In this embodiment, the gNB and UE confirm and agree to this subscription. After the subscription is established, the sensing entity immediately performs an initial sensing capability report. The gNB and UE report their comprehensive sensing capabilities to the Edge-SCF, covering multiple capability dimensions such as G (geometry), Q (signal quality), P (performance), and R (resources) as previously defined.

[0237] The relevant content of each dimension of perception ability is shown in the table below: Table 2 Scoring Dimensions and Ability Summary

[0238] S310: The sensing entity gNB maintains its sensing capability reporting to the Edge-SCF.

[0239] S311: The sensing entity UE maintains its sensing capability reporting to Edge-SCF.

[0240] In this embodiment, the sensing entity continuously maintains and updates its sensing capabilities. Specifically, during the service period, the sensing entity continuously reports its capabilities to Edge-SCF according to the update mechanism set at the time of subscription. These updates are typically triggered by specific events (such as location changes exceeding a threshold) or performed at fixed intervals to ensure that the capability information held by the network side is up-to-date.

[0241] The update of the geometric score (G) combines event triggering with periodic reporting. For cases of significant changes in position or orientation, an event trigger threshold is set: when the position change exceeds 50-100 meters or the orientation change exceeds 30 degrees, immediate reporting is required. Simultaneously, periodic updates are implemented based on the mobility of the sensed entity: mobile sensed entities report at shorter intervals of 1 to 5 seconds, while stationary sensed entities update at longer intervals of 30 to 300 seconds.

[0242] The wireless signal measurements upon which the signal quality score (Q) depends fully reuse the L3 measurement reporting mechanism in existing communication systems, without introducing new reporting procedures. Its updates follow established A3 / A5 measurement event triggering conditions or periodic reporting configurations.

[0243] Capability and performance score (Q, P) related information, including hardware capabilities, supported sensing modes, available measurements, and accuracy metrics, are all considered static or semi-static capabilities. This information is primarily reported in full once during the initial registration of the gNB or UE with the network. Subsequent updates are only triggered when device capabilities change (e.g., through software upgrades).

[0244] The update mechanism for Resource Points (R) varies depending on the monitored object. For UE battery status, an event-triggered approach is used, typically reporting when the battery level changes by more than 5-10% or reaches a preset low battery threshold. For gNB and UE computing load, computing power, and PRB utilization, a combination of periodic monitoring and event-triggered methods is recommended: computing load should be reported when the change exceeds 20%, or at a period of 5-10 seconds; PRB utilization should be reported at a period of 1-5 seconds, or when the change exceeds 15%.

[0245] The reputation score (T) is a special case. It is calculated and maintained internally by the network-side perception control function based on historical task execution results (such as success rate and latency), without involving any inter-entity signaling reporting from the gNB or UE.

[0246] S312: Edge-SCF maintains a sensing capability database, which includes the sensing capabilities reported by each sensing entity and their update timestamps.

[0247] In this embodiment, Edge-SCF maintains a centralized, dynamically updated perception capability database. This database aggregates the latest perception capability information of all subordinate perception entities, laying the foundation for the optimal allocation and execution of perception tasks. For example, perception capability data includes: Perception Entity 1 - Perception Capability Information 1 - Time 1; Perception Entity 2 - Perception Capability Information 1 - Time 2; Perception Entity 3 - Perception Capability Information 3 - Time 3.

[0248] The following will introduce how different ability items in perception are calculated: (1) Geometric fractions Based on the Geometric Dilution Factor (GDOP), the relative position, orientation, and topological advantages and disadvantages of sensed entities compared to the target are quantified. GDOP takes into account the spatial distribution of multiple sensed entities when they cooperate, avoiding accuracy loss caused by collinearity or clustering.

[0249] GDOP is calculated by measuring the relative geometric relationship between a sensing entity (such as a base station) and a target. For example, there are M sensing entities (such as base stations) involved in the localization of a single target.

[0250] ① Construct the geometric matrix: The target's position is , No. The location of each perceived entity is Then the target is the first The distance between the entities is:

[0251] The corresponding unit direction vector (cosine) is:

[0252] ② For three-dimensional positioning, geometric matrix It is The matrix (considering clock skew):

[0253] ③GDOP is defined as the square root of the sum of the elements on the main diagonal of the covariance matrix. Assuming that all measurement errors are independent and identically distributed, we have:

[0254] in, This is the accuracy matrix, where the diagonal elements correspond to the target positions. And the variance amplification factor for clock skew estimation. It is to find the trace of a matrix (the sum of its diagonal elements).

[0255] ④ After obtaining the GDOP, map it to a fraction between 0 and 1. The smaller the GDOP, the better the geometry. Common mappings are:

[0256] In this way, when (In the ideal configuration) ; The larger, The closer it gets to 0.

[0257] ⑤ Use of the capability field: Location information (current position): This is the direct input for calculating the unit direction vector and distance, and is the basic data for constructing the geometric matrix H.

[0258] Movement state (velocity, direction vector): does not directly participate in GDOP calculation, but is used to trigger the mechanism. When the movement of a sensed entity or target causes a position change exceeding a threshold (e.g., 50 meters) or a significant change in direction (e.g., 30 degrees), the geometric score G is recalculated and reported to ensure timely scoring.

[0259] (2) Signal quality fraction Q Signal quality score reflects the reliability of the wireless link between the sensed entity and the target, and is calculated based on communication metrics in the real-time measurement report: ① Map RSRP to [0,1]:

[0260] in,

[0261] ② Map SINR to [0,1]:

[0262] in,

[0263] ③ Map CQI to [0,1]:

[0264] in, .

[0265] ④ In summary:

[0266] The weights are adjusted according to frequency bands, antenna configurations, etc., and are set to equal weights by default.

[0267] ⑤ Use of the capability field: Real-time measurements (RSRP, SINR, CQI) are used directly as the core input for Q-score calculation, generating real-time scores through a weighted normalization formula.

[0268] Radio frequency capabilities (frequency band, transmit power) are used indirectly: typically as basic capability labels or weighting factors. When calculating Q-scores, measurements from different frequency bands may be normalized and calibrated; transmit power can be used to determine whether the link budget meets the minimum requirements for sensing tasks, serving as an initial screening criterion.

[0269] Antenna system capabilities (whether it supports MIMO) are indirectly used as a performance potential factor. For example, entities with strong massive MIMO and beamforming capabilities may be given higher confidence weights or used as a qualification for supporting higher-order sensing modes at the same RSRP / SINR.

[0270] (3) Perceptual performance score P ① The perception performance score reflects the theoretical accuracy and capability of a sensing entity when executing a certain sensing mode, and is calculated based on the static / semi-static capability information reported by the sensing entity:

[0271] in, This represents the theoretical ranging / angle measurement error based on this sensing mode; The score is normalized based on the maximum detection range and field of view; the weight can be adjusted according to the mission type (e.g., high-precision missions emphasize accuracy).

[0272] ②Usage of the capability field: Supported sensing modes (for direct use) are a prerequisite for P-score calculation. Each sensing mode corresponds to a specific set of precision and range parameters.

[0273] Perception measurement capabilities (direct use): Determining what measurements (ToA, AoA, etc.) a sensing entity can provide in a specific sensing mode is key to assessing its suitability for a particular sensing task.

[0274] The measurement accuracy index (used directly) is the core data source for the "Accuracy_Score" part of the P-score, and directly determines the theoretical performance score of the sensing entity in this sensing mode.

[0275] The perceived range (used directly) is the core data source for the "Range_Score" part of the P-score, used to evaluate its coverage capability.

[0276] (4) Resource score R ① It reflects the current availability of resources (PRB, CPU, energy, etc.) of the candidate entity and is highly correlated with whether it can meet the requirements of low latency / high sampling rate.

[0277]

[0278] in: Battery status normalization; Remaining available computing power percentage; The remaining PRB ratio of the sensing-dedicated BWP; the weight can be adjusted according to the type of sensing entity (e.g., the terminal focuses on energy consumption, and the base station focuses on PRB).

[0279] ②Usage of the capability field: Energy status (direct use): converted to Energy_Score, which is a component of R score.

[0280] Computing power (direct use): converted to CPU_Score, which is a component of R score.

[0281] Resource usage status (direct use): converted to PRB_Score, which is a component of R score.

[0282] (5) Credit Score T It reflects historical service fulfillment capabilities (success rate, average response time, and anomaly rate) and trustworthiness; ①Exponential Moving Average (EMA):

[0283] in, Historical success rate of the mission; This is the average reporting delay; Packet loss rate; The reference latency can be set to the latency required by the task side.

[0284] ②Use of the capability field It does not rely on reports from perceived entities. It is calculated internally by the network side (Edge-SCF) based on records (success rate, latency) of historical sensing tasks.

[0285] The above embodiments illustrate how the first core network element executes a sensing task when it receives the task. In some scenarios, the first core network element cannot independently complete the sensing task and needs to collaborate with first core network elements in other autonomous systems. The relevant implementation of cross-domain collaboration will be introduced below.

[0286] Specifically, if the eighth preset condition is met, the first core network element sends a first message to the second core network element, which instructs the initiation of cross-domain collaboration to complete the perception task; the second core network element determines the scheduling result based on the first message, which includes the set of cross-domain perception nodes that collaborate to complete the perception task; the second core network element sends a second message to the first core network element, which includes the scheduling result.

[0287] In some implementations, after obtaining the scheduling result, the first core network element can also send a third message to the second core network element, which is used to instruct the first core network element to provide feedback on the scheduling result.

[0288] If the third message indicates that the scheduling result can be executed, the second core network element records the scheduling result.

[0289] If the third message indicates that the scheduling result is not allowed to be executed, the second core network element executes the rollback strategy.

[0290] 1. When any of the following conditions (eighth preset condition) are met, Edge-SCF will initiate cross-domain collaboration and push the perception task up to Core-SCF: 1) The local HotSet cannot meet the QoS requirements of the sensing task (including accuracy, coverage, latency budget, etc.), and there are still no feasible candidates when expanding through the ColdSet; 2) Local domain resources are limited (e.g., insufficient available PRB, CPU load, or energy budget), so even if there are candidates in the HoSett / ColdSet, the task cannot be completed within budget; 3) The perception task itself has cross-domain attributes, such as the target moving across multiple edge domains, requiring multi-domain collaborative perception, or involving multiple Edge-SCF jurisdictions; 4) Perception tasks have high priority and require global optimization or cross-domain collaborative strategies (such as high-priority safety tasks, vehicle-road cooperative positioning tasks, etc.). 5) Local decision-making is approaching its latency limit; continuing the search within the local domain may not meet the task's T_budget requirements. 6) The application server explicitly requires the use of cross-domain resources, or the business policy indicates that global scheduling should be used preferentially.

[0291] 2. The information thrown by Edge-SCF is sent in "compressed digest" format, and includes: (1) Task information summary: Task ID, Task type, QoS requirements, and remaining time budget (T_budget_remain); (2) Candidate set summary: The top L high-scoring candidates of HotSet and ColdSet (top-L) include the perceived entity number, recent score, location information, available resources (such as available PRB, CPU load, energy consumption estimate), visibility / coverage, and operational history reliability, etc. (3) Local domain resource load information: current PRB utilization, CPU usage, cache status, link availability, etc., used by Core-SCF to determine whether the local domain can continue to participate in collaboration; (4) Summary of local detection results: Reasons for failure to meet QoS in HotSet / ColdSet detection (such as insufficient coverage, high latency, insufficient energy budget, limited field of view, etc.). (5) Temporary constraints or preferences: whether cross-domain scheduling is allowed, constraints on application-side fixed patterns / fixed sensing entities, whether joint selection is supported, etc.; (6) Edge-SCF’s own time window limit: the maximum remaining processing time and response time available for collaboration.

[0292] The data being fed up is compressed and filtered to exclude information on all perceived entities across the entire domain, in order to reduce cross-domain signaling load and accelerate scheduling decisions on the core side.

[0293] 3. After receiving a cross-domain report from Edge-SCF, Core-SCF will perform the following tasks: 1) Aggregate candidate summaries from multiple edge domains to form a global view of cross-domain candidates; Ensure that the information being pushed up is valid and comes from a trusted Edge; perform a quick alignment and consistency check between the digest from that Edge and the known global state; aggregate the digests from one or more Edges into a pool of cross-domain candidates that can be scheduled; determine whether a task needs to participate across domains and which Edge domains should be included in the collaboration.

[0294] 2) Based on the remaining time budget and task priorities, decide whether to use a rapid heuristic (Partial Solution) or a joint optimization (Final Solution): If the budget is tight, generate a fast cross-domain partial solution and feed it back to Edge-SCF for further fine-tuning locally; If the budget allows, initiate a cross-domain joint optimization process (which can be done using a centralized solution or a distributed method with restricted iterations). 5) Generate scheduling results: including recommended patterns, cross-domain aware entity sets, resource reservations (such as the number of PRBs), and execution timing, and send the results to the leading Edge-SCF; 6) After Edge-SCF sends back the CommitAcK or Rollback command, execute the necessary adjustments or rollback strategies to ensure the task is ultimately implemented: If a CommitAck is received: Core will officially reserve the resources (write them to the global resource table) and record the collaborative instance as successfully prepared; then wait for the execution result to be reported.

[0295] If a Rollback is received: Core resolves the failure reason and takes the next step according to priority (e.g., select an alternative set, relax some resource allocation, rerun optimization, or push the task to the backup strategy); if a feasible solution cannot be found in a short time, Edge is notified to execute a degradation solution (e.g., reduce QoS).

[0296] 7) Record the results of cross-domain collaboration for subsequent global statistics, model training, and rapid decision-making for the next collaboration.

[0297] It is evident that the technical solutions provided by the embodiments of this application can achieve the following objectives: Based on the Hot / Cold edge candidate caching and dynamic promotion / demotion mechanism, a two-layer candidate set mechanism maintained at the edge control unit is proposed and implemented: a high-priority candidate set (HotSet) and a candidate set (ColdSet), as well as a promotion / demotion and lifecycle TTL control strategy based on event-driven and periodic heartbeat.

[0298] Task-driven scoring method: Define a five-dimensional scoring vector [G,Q,P,R,T], and calculate the weight of each dimension according to the input service request through a mapping function. Then, sort the candidate perceived entities or patterns based on weighted sum / softmax and output the Top-K candidate set.

[0299] The collaborative selection method provides three decision-making processes: (i) selecting patterns by scoring when the set of perceived entities is fixed; (ii) selecting perceived entities by Top-K scoring when the pattern is fixed; and (iii) performing perceived entity screening in parallel for all candidate patterns and evaluating the joint utility of (mode, node_set) to select the optimal combination when there is no prior knowledge. Furthermore, it restricts the joint selection to use a two-stage coarse screening + fine screening approximation algorithm to meet the real-time constraints.

[0300] It should be noted that the technical solution provided in this application can be applied to other scenarios, which may specifically include: 1. Intelligent factory indoor high-speed AGV precise obstacle avoidance Actual equipment: 4 fixed 5G indoor small base stations (gNB-IDU, which have been in service for a long time) at the four corners of the ceiling, and 1 laser + ISAC dual-mode AGV with a speed of 2-3 m / s (as a sensing target). Environment: Dense metal shelving, severe multi-path design with dynamic obstruction. QoS requirements: Positioning accuracy ≤10cm, update rate 100Hz, end-to-end latency <5ms Table 3 Comparison Table

[0301] As shown in the table above, switching only the perception mode without switching the perceived entity greatly reduces the switching latency.

[0302] 2. Blind spot warning system for large trucks at highway entrances: Three roadside units (RSUs) (gNB type) are mounted on the roadside pole. Real-world setup: Two millimeter-wave base stations mounted on streetlight poles, and 10-15 intelligent connected vehicles supporting ISAC (which can temporarily act as Rx sensing entities) connected in real time at the intersection. Target: A car obscured by a large truck (speed 80-120 km / h) QoS requirements: Lateral positioning accuracy <0.5m, update rate 50Hz Table 4 Comparison Table

[0303] As shown in the table above, switching only the sensing entity without switching the sensing mode reduces the switching latency, and the vehicle's temporary participation does not affect normal communication.

[0304] 3. Low-altitude drone swarms traversing urban skyscraper canyons (a hallmark of 6G technology) - Real-world equipment: Actual equipment: 6 ground-based macro base stations + 4 rooftop millimeter-wave small stations, and 30 logistics / police drones supporting ISAC (some of which can act as Tx / Rx relays) in the formation. Objective: Real-time perception of the overall three-dimensional trajectory of the formation, at a speed of 50-100 km / h and an altitude of 30-150 m. QoS requirements: 3D positioning accuracy <1m, velocity error <0.3m / s, update rate 50Hz, no interruptions. Table 5 Comparison Table

[0305] As can be seen from the table above, only joint selection can break out of the local optimum, improve overall performance compared to existing technologies, and be uninterrupted throughout the process.

[0306] It should be understood that Figures 1 to 3 The flowcharts or scene diagrams shown are for illustrative purposes only and are not intended to limit the embodiments of this application to the examples illustrated. In fact, those skilled in the art can interpret the embodiments based on... Figures 1 to 3 The examples in the document can be transformed into equivalent ways to obtain more implementations.

[0307] The above text combined Figures 1 to 3 This document describes in detail the communication method provided in the embodiments of this application. The following will combine... Figures 4 to 5 The device embodiments of this application are described in detail below. It should be understood that the communication device of this application embodiment can execute the various communication methods of the foregoing embodiments of this application, that is, the specific working processes of the various products below can be referred to the corresponding processes in the foregoing method embodiments.

[0308] In the embodiments described above, the terminal device may execute some or all of the steps in each embodiment; the network device may execute some or all of the steps in each embodiment. These steps or operations are merely examples, and the embodiments of this application may also perform other operations or variations thereof. Furthermore, the steps may be executed in different orders as presented in the embodiments, and it is not necessary to execute all the operations in the embodiments of this application. Moreover, the sequence number of each step does not imply the order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0309] Figure 4 This is a schematic block diagram of a communication device provided in an embodiment of this application. Figure 4 As shown, the communication device 400 may include a communication module 420. The communication module 420 can implement corresponding communication functions, which can be internal communication functions of the communication device 400 or communication functions between the communication device 400 and other devices. Optionally, the communication module 420 may also be referred to as a communication interface or transceiver module. Optionally, the communication device 400 may also include a processing module 410. The processing module 410 can implement corresponding processing functions.

[0310] Optionally, the communication device 400 further includes a storage module, which can be used to store instructions and / or data; the processing module 410 can read the instructions and / or data in the storage module so that the communication device 400 can implement the aforementioned method embodiments.

[0311] In one possible design, the communication device 400 may correspond to the first core network element in the above method embodiments, or a component (such as a circuit, chip, or chip system) configured in the first core network element. The communication device 400 can be used to execute the steps or processes performed by the first core network element in any of the above method embodiments.

[0312] For example, the communication module 420 is used to receive a sensing task sent by a second core network element, the sensing task including a quality of service requirement, the second core network element managing the first core network element; Processing module 410 is used to determine a perception strategy based on the service quality requirements. The perception strategy is used to indicate the selection of a perception mode and / or a perception node for the perception task. The perception node is used to execute the perception task. The perception mode is used to indicate the manner in which the perception node executes the perception task. The processing module 410 is used to determine, according to the perception strategy, the perception node and / or perception mode that match the perception task, wherein the perception node is a perception node in the first set of perception nodes managed by the first core network element.

[0313] In some implementations, the processing module 410 is specifically configured to, if the perception strategy indicates that a perception mode is selected for the perception task, obtain a first score corresponding to each perception mode under the first set of perception nodes; and determine the perception mode whose first score satisfies a first preset condition as the perception mode that matches the perception task.

[0314] In some implementations, the processing module 410 is specifically configured to determine the weight corresponding to each parameter in the first parameter set according to the service quality requirements, thereby obtaining a first weight set. The first parameter set includes at least one parameter, which reflects the capabilities of the first sensing node. The first sensing node is any sensing node in the first sensing node set. The parameters in the first parameter set correspond one-to-one with the weights in the first weight set. Based on at least some of the parameters in the first parameter set and at least some of the weights in the first weight set, a first score corresponding to a sensing mode is determined.

[0315] In some implementations, the processing module 410 is specifically configured to obtain a first vector based on the service quality requirements, the first vector including vectors corresponding to each indicator in the service quality requirements; and determine a first weight set based on the correlation matrix and the first vector, the correlation matrix being the correlation matrix between the service quality requirements and the first parameter set, wherein an item in the correlation matrix is ​​used to indicate the degree of influence of an indicator in the service quality requirements on a parameter in the first parameter set.

[0316] In some implementations, the processing module 410 is specifically configured to, if the perception strategy indicates that a perception node is selected for the perception task, obtain a second score corresponding to the first perception node in the target perception mode, wherein the first perception node is any one of the first perception node sets, and the target perception mode indicates the perception mode currently supported by the first perception node set; and determine the first perception node whose second score meets the second preset condition as the perception node matching the perception task.

[0317] In some implementations, the processing module 410 is specifically used to determine the weight corresponding to each parameter in the first parameter set according to the service quality requirements, to obtain a first weight set, wherein the first parameter set includes at least one parameter, the parameter reflecting the capability of the first sensing node, and the parameters in the first parameter set correspond one-to-one with the weights in the first weight set; and to determine a second score corresponding to the first sensing node according to at least some of the parameters in the first parameter set and at least some of the weights in the first weight set.

[0318] In some implementations, the processing module 410 is specifically configured to: if the perception strategy indicates that the perception task simultaneously selects a perception mode and a perception node, obtain a third score corresponding to a second perception node under the first perception mode, wherein the second perception node is any one of the first perception node sets, and the first perception mode is any one of the perception modes supported by the first perception node set; determine a second perception node whose third score under the first perception mode meets a third preset condition; obtain a fourth score corresponding to a first perception combination, wherein the first perception combination includes the first perception mode and a second perception node set, and the second perception node set includes second perception nodes that meet the second preset condition; and determine the first perception combination whose fourth score meets the fourth preset condition as a perception node and perception mode that matches the perception task.

[0319] In some implementations, the processing module 410 is specifically used to sum up the third scores corresponding to each second sensing node in the second sensing node set to obtain a first calculation result; to obtain a second calculation result based on the target index corresponding to each second sensing node in the second sensing node set, wherein the target index is used to indicate the cost required to use the first sensing mode and the second sensing node in combination; and to use the difference between the first calculation result and the second calculation result as the fourth score corresponding to the first sensing combination.

[0320] In some implementations, the processing module 410 is specifically configured to determine the weight corresponding to each parameter in the first parameter set according to the service quality requirements, thereby obtaining a first weight set. The first parameter set includes at least one parameter, which reflects the capabilities of the first sensing node, and the first sensing node is any one of the sensing nodes in the set of sensing nodes. Based on at least some of the parameters in the first parameter set and at least some of the weights in the first weight set, a score for the sensing strategy is obtained. Based on the score of the sensing strategy, a sensing strategy matching the sensing task is determined.

[0321] In some implementations, the processing module 410 is specifically configured to: if the score of the first perception strategy meets a fifth preset condition, determine that the perception strategy matching the perception task is the first perception strategy, and the first perception strategy indicates that the perception task selects a perception mode; if the score of the first perception strategy does not meet the fifth preset condition and the score of the second perception strategy meets a sixth preset condition, determine that the perception strategy matching the perception task is the second perception strategy, and the second perception strategy indicates that the perception task selects a perception node; if the score of the second perception strategy does not meet the sixth preset condition and the score of the third perception strategy meets a seventh preset condition, determine that the perception strategy matching the perception task is the third perception strategy, and the third perception strategy indicates that the perception task selects a perception mode and a perception node.

[0322] In some implementations, the first set of parameters includes one or more of the following: Geometric fraction, which is used to indicate the spatial positional relationship between the sensing node and the sensed target, and / or the spatial positional relationship between the sensing node and other sensing nodes; Signal quality score, which is used to indicate the communication quality of the wireless link between the sensing node and the sensed target; A perception performance score, which indicates the potential accuracy and capability of the perception entity in performing the perception task; Resource score, which indicates the sufficiency of available resources in the sensing node.

[0323] A reputation score, which is used to indicate the performance and reliability of the sensing node when performing historical sensing tasks.

[0324] In some implementations, the sensing task further includes time constraint information, which includes one or more of the following: the timing of the execution of the sensing task, the expected runtime of the sensing task, and the maximum feedback delay.

[0325] In some implementations, the communication module 420 is further configured to send a first message to the second core network element if an eighth preset condition is met, wherein the first message indicates that cross-domain collaboration is initiated to complete the sensing task; The communication module 420 is also used to receive a second message sent by the second core network element, the second message including a scheduling result, the scheduling result indicating a set of cross-domain sensing nodes that cooperate to complete the sensing task.

[0326] In some implementations, the communication module 420 is further configured to send a third message from the first core network element to the second core network element, the third message being used to indicate feedback on the scheduling result.

[0327] In some implementations, the eighth preset condition may include one or more of the following: The first set of sensing nodes cannot meet the quality of service requirements; or... The local domain resources corresponding to the first core network element cannot meet the service quality requirements; or... The perception task includes first indication information, which indicates cross-domain completion; or... The priority of the perception task meets a preset condition; or, The first set of sensing nodes cannot meet the maximum feedback latency corresponding to the sensing service.

[0328] The above are merely examples; for detailed steps or procedures, please refer to the descriptions in the foregoing embodiments.

[0329] In one possible design, the communication device 400 may correspond to the second core network element in the above method embodiments, or a component (such as a circuit, chip, or chip system) configured in the second core network element. The communication device 400 can be used to execute the steps or processes performed by the second core network element in any of the above method embodiments.

[0330] The communication module 420 is used to acquire a sensing task, the sensing task including a quality of service requirement, the sensing task indicating that objects in the target area should be sensed; The processing module 410 is used to determine the first core network element that will perform the sensing task based on the sensing task. The communication module 420 is used to send the sensing task to the first core network element. The quality of service requirement is used to instruct the first core network element to determine the sensing strategy. The sensing strategy is used to instruct the first core network element to select a sensing mode and / or a sensing node for the sensing task. The sensing node is used to execute the sensing task. The sensing mode is used to instruct the sensing node to execute the sensing task in a certain way. The sensing node is a sensing node in the first set of sensing nodes managed by the first core network element.

[0331] In some implementations, the processing module 410 is specifically configured to determine the first core network element to perform the sensing task based on the location of each first core network element and the target area, wherein the service area corresponding to the first core network element includes the target area; and / or, determine the first core network element to perform the sensing task based on the computing power and / or load of each first core network element; and / or, determine the first core network element to perform the sensing task based on the historical information of each first core network element, wherein the historical information is used to indicate the relevant log records when the first core network element performs historical sensing tasks.

[0332] In some implementations, the historical information includes one or more of the following: task success rate, average response latency, cross-domain collaboration rate, and reputation score.

[0333] In some implementations, the communication module 420 is also used to receive a first message sent by the first core network element, the first message indicating the initiation of cross-domain collaboration to complete the sensing task; The processing module 410 is further configured to determine a scheduling result based on the first message, wherein the scheduling result includes a set of cross-domain sensing nodes that collaboratively complete the sensing task; The communication module 420 is also used to send a second message to the first core network element, the second message including the scheduling result.

[0334] In some implementations, the communication module 420 is further configured to receive a third message sent by the first core network element, the third message being used to indicate the feedback of the first core network element to the scheduling result; The processing module 410 is further configured to record the scheduling result if the third message indicates that the scheduling result is allowed to be executed; and to execute a rollback strategy if the third message indicates that the scheduling result is not allowed to be executed.

[0335] In some implementations, if the perception strategy indicates that a perception mode is selected for the perception task, the perception mode that matches the perception task refers to the perception mode whose first score meets the first preset condition, and the first score of the perception mode is the score obtained under the first set of perception nodes.

[0336] In some implementations, the first score corresponding to the perception mode is determined based on at least some parameters in a first parameter set and at least some weights in a first weight set. The first weight set is determined according to the service quality requirements. The first weight set corresponds one-to-one with the first parameter set. The first parameter set includes at least one parameter that reflects the capabilities of the first perception node. The first perception node is any one of the perception nodes in the first perception node set.

[0337] In some implementations, the first weight set is determined based on an association matrix and a first vector, which is determined based on service quality requirements. The first vector includes vectors corresponding to each indicator in the service quality requirements. The association matrix is ​​the relationship between the service quality requirements and the first parameter set, where each element indicates the degree of influence of an indicator in the service quality requirements on that parameter in the first parameter set.

[0338] In some implementations, if the perception strategy indicates the selection of a perception node for the perception task, then the perception node matching the perception task refers to the first perception node whose second score meets the second preset condition. Here, the second score of the first perception node is obtained under the target perception mode, the first perception node is any one of the first perception node sets, and the target perception mode indicates the perception mode currently supported by the first perception node set.

[0339] In some implementations, the second score of the first sensing node is determined based on at least some parameters in a first parameter set and at least some weights in a first weight set, the first weight set being determined according to the service quality requirements. The first weight set corresponds one-to-one with the first parameter set, the first parameter set including at least one parameter reflecting the capabilities of the first sensing node.

[0340] In some implementations, if the perception strategy is to select both a perception mode and a perception node for the perception task, the perception node and perception mode matching the perception task are the perception nodes and perception modes in the first perception combination that satisfy the fourth preset condition for the fourth score. The first perception combination includes a first perception mode and a second set of perception nodes. The second set of perception nodes includes second perception nodes that satisfy the third preset condition for the third score. The third score of the second perception node refers to the score of the second perception node under the first perception mode. The first perception mode is any perception mode supported by the first set of perception nodes, and the second perception node is any one of the perception nodes in the first set of perception nodes.

[0341] In some implementations, the fourth score of the first sensing combination is determined based on a first calculation result and a second calculation result. The first calculation result is obtained by the first core network element by summing the third scores corresponding to each second sensing node in the second sensing node set. The second calculation result is determined by the first core network element based on the target index corresponding to each second sensing node in the second sensing node set. The target index is used to indicate the cost required to jointly use the first sensing mode and the second sensing nodes.

[0342] In some implementations, the perception strategy matched with the perception task is determined based on the perception strategy score. The perception strategy score is determined by the first core network element based on at least some parameters in the first parameter set and at least some weights in the first weight set. The first weight set is determined based on the service quality requirements. The first weight set corresponds one-to-one with the first parameter set. The first parameter set includes at least one parameter that reflects the capabilities of the first perception node.

[0343] In some implementations, if the score of the first perception strategy meets the fifth preset condition, the perception strategy that matches the perception task is the first perception strategy, and the first perception strategy indicates the selection of a perception mode for the perception task. If the score of the first perception strategy does not meet the fifth preset condition and the score of the second perception strategy meets the sixth preset condition, the perception strategy that matches the perception task is the second perception strategy, and the second perception strategy indicates the selection of a perception node for the perception task. If the score of the second perception strategy does not meet the sixth preset condition and the score of the third perception strategy meets the seventh preset condition, the perception strategy that matches the perception task is the third perception strategy, and the third perception strategy indicates the selection of perception mode and perception node for the perception task.

[0344] In some implementations, the first set of parameters includes one or more of the following: Geometric fraction, which is used to indicate the spatial positional relationship between the sensing node and the sensed target, and / or the spatial positional relationship between the sensing node and other sensing nodes; Signal quality score, which is used to indicate the communication quality of the wireless link between the sensing node and the sensed target; A perception performance score, which indicates the potential accuracy and capability of the perception entity in performing the perception task; Resource score, which indicates the sufficiency of available resources in the sensing node.

[0345] A reputation score, which is used to indicate the performance and reliability of the sensing node when performing historical sensing tasks.

[0346] In some implementations, the sensing task further includes time constraint information, which includes one or more of the following: the execution timing of the sensing task, the expected runtime of the sensing task, and the maximum feedback delay. The above are merely examples; detailed steps or processes can be found in the descriptions of the foregoing embodiments.

[0347] Figure 5 This is another schematic block diagram of the communication device 500 provided in the embodiments of this application. The communication device 500 may be a chip, chip system, or processor, etc., in a terminal device or network device that implements the above-described methods. The communication device 500 can be used to implement the methods described in the above-described method embodiments; for details, please refer to the descriptions in the above-described method embodiments.

[0348] like Figure 5As shown, the communication device 500 may include one or more processors 510, which may also be referred to as processing units or processing modules, and can implement certain control functions. The processor 510 may be a general-purpose processor or a dedicated processor, such as a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, while the central processing unit can be used to control the communication device 500 (e.g., a base station, baseband chip, user, user chip), execute software programs, and process data from the software programs.

[0349] In an alternative design, the processor 510 may also store instructions and / or data that can be executed by the processor 510 to cause the communication device 500 to perform the methods described in the above method embodiments.

[0350] In another alternative design, the communication device 500 may include a communication interface 520 for implementing receiving and transmitting functions. For example, the communication interface 520 may be a transceiver circuit, interface, interface circuit, or transceiver. The transceiver circuit, interface, interface circuit, or transceiver for implementing receiving and transmitting functions may be separate or integrated. The aforementioned transceiver circuit, interface, interface circuit, or transceiver may be used for reading and writing code / data, or it may be used for transmitting or relaying signals.

[0351] Optionally, the communication device 500 may include one or more memories 530, which may store instructions that can be executed on the processor 510, causing the communication device 500 to perform the methods described in the above method embodiments. Optionally, the memories 530 may also store data. Optionally, the processor 510 may also store instructions and / or data. The processor 510 and the memories 530 may be provided separately or integrated together.

[0352] It should be understood that, in one possible design, the steps in the method embodiments provided in this application can be implemented by integrated logic circuits in the processor's hardware or by instructions in software form. The steps of the methods disclosed in the embodiments of this application can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, detailed descriptions are not provided here.

[0353] In one implementation, the communication device 500 may correspond to the first core network element in the above method embodiments, and may be used to execute the various steps and / or processes executed by the first core network element in the above method embodiments. The processor 510 may be used to execute instructions stored in the memory 530, and when the processor 510 executes the instructions stored in the memory, the processor 510 is used to execute the various steps and / or processes of the above method embodiments corresponding to the first core network element.

[0354] In another implementation, the communication device 500 may correspond to the second core network element in the above method embodiments, and may be used to execute the various steps and / or processes executed by the second core network element in the above method embodiments. The processor 510 may be used to execute instructions stored in the memory 530, and when the processor 510 executes the instructions stored in the memory, the processor 510 is used to execute the various steps and / or processes of the above method embodiments corresponding to the second core network element.

[0355] It should be understood that the aforementioned processing device can be one or more chips. For example, the processing device can be a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), a system-on-chip (SoC), a central processor unit (CPU), a network processor (NP), a digital signal processor (DSP), a microcontroller unit (MCU), a programmable logic device (PLD), or other integrated chips.

[0356] It is understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM). It should be noted that the memory used in the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0357] According to the method provided in the embodiments of this application, this application also provides a chip system, which includes one or more processors for calling and executing instructions stored in memory, thereby causing the method described in the embodiments of this application to be executed. The chip system may be composed of chips or may include chips and other discrete devices.

[0358] The chip system may include input circuits or interfaces for transmitting information or data, and output circuits or interfaces for receiving information or data.

[0359] According to the method provided in the embodiments of this application, this application also provides a communication system, which includes the aforementioned first core network element and second core network element.

[0360] According to the method provided in the embodiments of this application, this application also provides a computer program product, which includes: computer program code, which, when run on a computer, causes the computer to execute the various steps or processes executed by the first core network element and the second core network element in any of the foregoing method embodiments.

[0361] According to the method provided in the embodiments of this application, this application also provides a computer-readable storage medium storing program code. When the program code is run on a computer, it causes the computer to execute the various steps or processes executed by the first core network element and the second core network element in any of the foregoing method embodiments.

[0362] The computer-readable storage medium may be the aforementioned volatile memory or non-volatile memory, or it may include both volatile memory and non-volatile memory.

[0363] In the embodiments of this application, the terms and English abbreviations are exemplary examples given for ease of description and should not be construed as limiting the application in any way. This application does not preclude the possibility of defining other terms that can achieve the same or similar functions in existing or future agreements.

[0364] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When these computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated.

[0365] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0366] It should be understood that in the various embodiments of this application, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0367] In summary, the above description is merely a preferred embodiment of the technical solution of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A communication method, characterized in that, The method includes: The first core network element receives a sensing task sent by the second core network element, the sensing task including quality of service requirements, and the second core network element manages the first core network element; The first core network element determines a perception strategy based on the quality of service requirements. The perception strategy is used to indicate the selection of a perception mode and / or a perception node for the perception task. The perception node is used to execute the perception task. The perception mode is used to indicate the manner in which the perception node executes the perception task. The first core network element determines the sensing node and / or sensing mode that matches the sensing task according to the sensing strategy, wherein the sensing node is a sensing node in the first set of sensing nodes managed by the first core network element.

2. The method according to claim 1, characterized in that, The first core network element determines, according to the perception strategy, the perception node and / or perception mode matching the perception task, including: If the perception strategy indicates that a perception mode is selected for the perception task, the first core network element obtains the first score corresponding to each perception mode under the first set of perception nodes. The first core network element determines the perception mode that meets the first preset condition in the first score as the perception mode that matches the perception task.

3. The method according to claim 2, characterized in that, The first core network element obtains the first score corresponding to each sensing mode under the first set of sensing nodes, including: The first core network element determines the weight corresponding to each parameter in the first parameter set according to the service quality requirements, and obtains the first weight set. The first parameter set includes at least one parameter, which reflects the capabilities of the first sensing node. The first sensing node is any sensing node in the first sensing node set. The parameters in the first parameter set correspond one-to-one with the weights in the first weight set. The first core network element determines a first score corresponding to a perception mode based on at least some parameters in the first parameter set and at least some weights in the first weight set.

4. The method according to claim 3, characterized in that, The first core network element determines the weight corresponding to each parameter in the first parameter set based on service quality requirements, thereby obtaining a first weight set, including: The first core network element obtains a first vector based on the service quality requirements, and the first vector includes vectors corresponding to each indicator in the service quality requirements; The first core network element determines the first weight set based on the correlation matrix and the first vector. The correlation matrix refers to the correlation matrix between the service quality requirement and the first parameter set. One item in the correlation matrix is ​​used to indicate the degree of influence of an indicator in the service quality requirement on a parameter in the first parameter set.

5. The method according to claim 1, characterized in that, The first core network element determines, according to the perception strategy, the perception node and / or perception mode matching the perception task, including: If the perception strategy indicates that a perception node is selected for the perception task, the first core network element obtains the second score corresponding to the first perception node in the target perception mode. The first perception node is any one of the first perception node sets, and the target perception mode indicates the perception mode currently supported by the first perception node set. The first core network element determines the first sensing node whose second score meets the second preset condition as the sensing node that matches the sensing task.

6. The method according to claim 5, characterized in that, The first core network element acquires the second score corresponding to the first sensing node in the target sensing mode, including: The first core network element determines the weight corresponding to each parameter in the first parameter set according to the service quality requirements, and obtains the first weight set. The first parameter set includes at least one parameter, which reflects the capabilities of the first sensing node. The parameters in the first parameter set correspond one-to-one with the weights in the first weight set. The first core network element determines the second score corresponding to the first sensing node based on at least some of the parameters in the first parameter set and at least some of the weights in the first weight set.

7. The method according to claim 1, characterized in that, The first core network element determines, according to the perception strategy, the perception node and / or perception mode matching the perception task, including: If the perception strategy indicates that the perception task selects both a perception mode and a perception node, the first core network element obtains the third score corresponding to the second perception node in the first perception mode. The second perception node is any one of the perception nodes in the first set of perception nodes, and the first perception mode is any one of the perception modes supported by the first set of perception nodes. The first core network element determines the second sensing node whose third score meets the third preset condition under the first sensing mode. The first core network element obtains the fourth score corresponding to the first sensing combination. The first sensing combination includes the first sensing mode and the second sensing node set. The second sensing node set includes second sensing nodes that meet the third preset condition. The first core network element determines the first perception combination that meets the fourth preset condition in the fourth score as the perception node and perception mode that matches the perception task.

8. The method according to claim 7, characterized in that, The first core network element obtains the fourth score corresponding to the first sensing combination, including: The first core network element sums the third scores corresponding to each second sensing node in the second sensing node set to obtain the first calculation result; The first core network element obtains a second calculation result based on the target indicators corresponding to each second sensing node in the second sensing node set. The target indicators are used to indicate the cost required to use the first sensing mode and the second sensing nodes in combination. The first core network element uses the difference between the first calculation result and the second calculation result as the fourth score corresponding to the first sensing combination.

9. The method according to claim 1, characterized in that, The first core network element determines a perception strategy based on the quality of service requirements, including: The first core network element determines the weight corresponding to each parameter in the first parameter set according to the service quality requirements, and obtains the first weight set. The first parameter set includes at least one parameter, which reflects the capabilities of the first sensing node. The first sensing node is any one of the sensing nodes in the sensing node set. The first core network element obtains a score for the perception strategy based on at least some parameters in the first parameter set and at least some weights in the first weight set. The first core network element determines a perception strategy that matches the perception task based on the score of the perception strategy.

10. The method according to claim 1, characterized in that, The first core network element determines a perception strategy matching the perception task based on the scoring of the perception strategy, including: If the score of the first perception strategy meets the fifth preset condition, the first core network element is determined to be the first perception strategy that matches the perception task, and the first perception strategy indicates the selection of the perception mode for the perception task. If the score of the first perception strategy does not meet the fifth preset condition and the score of the second perception strategy meets the sixth preset condition, the first core network element determines that the perception strategy matching the perception task is the second perception strategy, and the second perception strategy indicates the selection of a perception node for the perception task. If the score of the second perception strategy does not meet the sixth preset condition and the score of the third perception strategy meets the seventh preset condition, the first core network element determines that the perception strategy matching the perception task is the third perception strategy, and the third perception strategy indicates the selection of perception mode and perception node for the perception task.

11. The method according to any one of claims 3, 6 or 9, characterized in that, The first set of parameters includes one or more of the following: Geometric fraction, which is used to indicate the spatial positional relationship between the sensing node and the sensed target, and / or the spatial positional relationship between the sensing node and other sensing nodes; Signal quality score, which is used to indicate the communication quality of the wireless link between the sensing node and the sensed target; A perception performance score, which indicates the potential accuracy and capability of the perception entity in performing the perception task; Resource score, which indicates the sufficiency of available resources in the sensing node; A reputation score, which is used to indicate the performance and reliability of the sensing node when performing historical sensing tasks.

12. The method according to claim 1, characterized in that, The sensing task also includes time constraint information, which includes one or more of the following: the execution timing of the sensing task, the expected runtime of the sensing task, and the maximum feedback delay.

13. The method according to claim 1, characterized in that, The method further includes: If the eighth preset condition is met, the first core network element sends a first message to the second core network element, and the first message indicates that cross-domain collaboration is initiated to complete the perception task; The first core network element receives a second message sent by the second core network element. The second message includes a scheduling result, which indicates a set of cross-domain sensing nodes that cooperate to complete the sensing task.

14. The method according to claim 13, characterized in that, The method further includes: The first core network element sends a third message to the second core network element, the third message being used to indicate feedback on the scheduling result.

15. The method according to claim 13 or 14, characterized in that, The eighth preset condition may include one or more of the following: The first set of sensing nodes cannot meet the quality of service requirements; or... The local domain resources corresponding to the first core network element cannot meet the service quality requirements; or... The perception task includes first indication information, which indicates cross-domain completion; or... The priority of the perception task meets a preset condition; or, The first set of sensing nodes cannot meet the maximum feedback delay corresponding to the sensing task.

16. A communication method, characterized in that, The method includes: The second core network element acquires a perception task, which includes a quality of service requirement and indicates that objects in the target area should be perceived. The second core network element determines the first core network element to execute the sensing task based on the sensing task. The second core network element sends the perception task to the first core network element. The quality of service requirement is used to instruct the first core network element to determine the perception strategy. The perception strategy is used to instruct the first core network element to select a perception mode and / or a perception node for the perception task. The perception node is used to execute the perception task. The perception mode is used to instruct the perception node to execute the perception task in a certain way. The perception node is a perception node in the first set of perception nodes managed by the first core network element.

17. The method according to claim 16, characterized in that, The second core network element determines the first core network element to execute the sensing task based on the sensing task, including: The second core network element determines the first core network element to perform the perception task based on the location of each first core network element and the target area, wherein the service area corresponding to the first core network element includes the target area; and / or, The second core network element determines the first core network element to perform the sensing task based on the computing power and / or load of each first core network element; and / or, The second core network element determines the first core network element to perform the perception task based on the historical information of each first core network element. The historical information is used to instruct the first core network element to record relevant logs when performing historical perception tasks.

18. The method according to claim 17, characterized in that, The historical information includes one or more of the following: task success rate, average response latency, cross-domain collaboration level, and reputation score.

19. The method according to claim 17, characterized in that, The method further includes: The second core network element receives a first message sent by the first core network element, the first message indicating the initiation of cross-domain collaboration to complete the sensing task; The second core network element determines the scheduling result based on the first message, and the scheduling result includes a set of cross-domain sensing nodes that cooperate to complete the sensing task; The second core network element sends a second message to the first core network element, the second message including the scheduling result.

20. The method according to claim 19, characterized in that, The method further includes: The second core network element receives a third message sent by the first core network element, the third message being used to instruct the first core network element to provide feedback on the scheduling result; If the third message indicates that the scheduling result is allowed to be executed, the second core network element records the scheduling result; If the third message indicates that the scheduling result is not allowed to be executed, the second core network element executes the rollback strategy.

21. A communication device, characterized in that, The device includes at least one processor coupled to a memory storing a program or instructions, the processor executing the program or instructions to cause the device to perform the method as described in any one of claims 1 to 20.

22. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed, they cause the computer to perform the method as described in any one of claims 1 to 20.

23. A communication system, characterized in that, Includes the communication device as described in claim 21.

24. A chip system, characterized in that, The chip system includes one or more processors, which are configured to retrieve and execute instructions stored in memory, such that the method as described in any one of claims 1 to 20 is performed.

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