A communication method, a communication device, and a communication system

Abstract

This application provides a communication method, communication device, and communication system. For example, the method is applicable to scenarios involving the transmission of sensing data. In this method, the sensing node uploads its communication cost, computing resource status, energy status, and the amount, mode, and acquisition time of the sensing data to the network device. Based on these parameters, the network device allocates uplink transmission resources for the sensing node to transmit the sensing data and dynamically determines the data processing method of the sensing node; achieving a balance between the amount of sensing data and its information value, improving the energy efficiency of the sensing node, extending the network lifetime, and adapting to the requirements of high reliability and low latency communication.

Description

Technical Field

[0001] This application relates to the field of wireless communication, and more particularly to a communication method, communication device, and communication system. Background Technology

[0002] With the development of enhanced 5G (5G-Advanced, 5G-A) and 6G sensing integration, wide-area heterogeneous sensor networks (such as smart cities and industrial IoT) are being widely used. A wide-area heterogeneous sensor network refers to an intelligent network system that spans multiple base stations geographically and is composed of various types of sensing devices, communication devices, and computing nodes, all under unified management. Wide-area heterogeneous sensor networks deploy a massive number of sensing devices, also known as sensing nodes, which are responsible for tasks such as data acquisition, local computing, and uplink transmission. Sensing nodes are used to collect sensing data, which is used to describe the state and characteristics of the physical world; examples include image data collected by cameras in autonomous driving systems, point cloud data acquired by LiDAR, and reflectance data obtained by millimeter-wave radar.

[0003] Wide-area heterogeneous sensor networks (WANs) contain a vast number of diverse sensing nodes, which can be mobile devices such as smartphones and wearables, roadside equipment, vehicle-mounted systems, cameras, radars, and sensors. These sensing nodes vary significantly in terms of computing power, bandwidth, energy consumption, and mobility. In such highly heterogeneous and resource-constrained WANs, efficiently transmitting sensing data is a key challenge that needs to be addressed. Summary of the Invention

[0004] This application provides a communication method, communication device, and communication system that can comprehensively consider the capabilities, status, and types of sensing nodes to dynamically transmit sensing data, enabling heterogeneous sensing nodes to adaptively upload sensing data.

[0005] To achieve the above objectives, the embodiments of this application adopt the following technical solutions:

[0006] Firstly, a communication method is provided. This method can be executed by a terminal device (sensing node), or by a component (such as a circuit, chip, or chip system) configured in the terminal device (sensing node), or by a logic module or software capable of implementing all or part of the functions of the terminal device (sensing node). This application does not limit this. The following description uses a terminal device (sensing node) as an example.

[0007] The method includes: a sensing node collecting sensing data; and sending a first message to a network device; the first message includes sensing node information and / or sensing data information, wherein the sensing node information includes at least one of first information, second information, and third information, and the sensing data information includes at least one of fourth information, fifth information, and sixth information. Specifically, the first information indicates the communication cost of the sensing node, the second information indicates the computing resource status of the sensing node, the third information indicates the energy status of the sensing node, the fourth information indicates the data mode of the sensing data, the fifth information indicates the data volume of the sensing data, and the sixth information indicates the acquisition time of the sensing data.

[0008] In this method, before transmitting sensing data to the network device, the sensing node sends its sensing node information and sensing data information to the network device. This allows the network device to allocate appropriate transmission resources to the sensing node based on the specific circumstances of the sensing node and the sensing data, and to decide whether the sensing node should perform local preprocessing. This ensures the quality of critical data, improves data transmission efficiency, and reduces resource overhead.

[0009] In conjunction with the first aspect, in one possible implementation, the sensing node also receives a second message from the network device; the second message includes first indication information and / or second indication information, the first indication information being used to indicate the data processing method allocated by the network device to the sensing node, such as feature extraction, compressed uploading, data sampling or downsampling, structured event description, or raw data uploading, and the second indication information being used to indicate the uplink transmission resources allocated by the network device to the sensing node.

[0010] In one possible implementation, at least one of the second, third, fourth, and fifth information is used by the network device to assign a data processing method to the sensing node.

[0011] In one possible implementation, at least one of the first, fourth, and fifth information is used by the network device to allocate uplink transmission resources to the sensing node.

[0012] In this method, based on the sensing node information and / or sensing data information reported by the sensing nodes, the network device can comprehensively analyze the received sensing node information and / or sensing data information to dynamically select the optimal data processing method and uplink transmission resources for each sensing node. This significantly extends the overall network uptime.

[0013] In conjunction with the first aspect, in one possible implementation, the sensing node processes the sensing data according to the data processing method indicated by the first indication information; and sends the processed sensing data to the network device on the uplink transmission resources indicated by the second indication information.

[0014] The sensing data, after local preprocessing at the sensing nodes, retains key information while reducing data volume and transmission burden, providing high-quality input for subsequent multimodal fusion.

[0015] In conjunction with the first aspect, in one possible implementation, the sixth information is used by network devices to associate sensing data from different data modalities. This provides the conditions for further fusion of sensing data from multiple data modalities.

[0016] In conjunction with the first aspect, in one possible implementation, the communication cost includes at least one of the following: reference signal received power, signal-to-interference-plus-noise ratio, latency, and bit error rate; the computing resource status includes at least one of the following: CPU utilization, GPU utilization, and available memory; and the energy status includes electricity.

[0017] Secondly, a communication method is provided, which can be executed by a network device, or by a component (such as a circuit, chip, or chip system) configured in the network device, or by a logic module or software capable of implementing all or part of the functions of the network device. This application does not limit this. The following description uses a network device as an example.

[0018] The method includes: sending a sensing task to a sensing node; the sensing task being used to trigger the sensing node to collect sensing data; receiving a first message from the sensing node; the first message including sensing node information and / or sensing data information, wherein the sensing node information includes at least one of first information, second information, and third information, and the sensing data information includes at least one of fourth information, fifth information, and sixth information, wherein the first information indicates the communication cost of the sensing node, the second information indicates the computing resource status of the sensing node, the third information indicates the energy status of the sensing node, the fourth information indicates the data mode of the sensing data, the fifth information indicates the data volume of the sensing data, and the sixth information indicates the collection time of the sensing data.

[0019] In conjunction with the second aspect, in one possible implementation, the network device assigns a data processing method to the sensing node based on at least one of the second, third, fourth, and fifth information; the data processing method includes feature extraction, compressed uploading, data sampling or downsampling, structured event description, or raw data uploading.

[0020] In conjunction with the second aspect, in one possible implementation, the network device allocates uplink transmission resources to the sensing node based on at least one of the first, fourth, and fifth information.

[0021] In conjunction with the second aspect, in one possible implementation, the network device sends a second message to the sensing node; the second message includes first indication information and / or second indication information, the first indication information being used to indicate the data processing method allocated to the sensing node, and the second indication information being used to indicate the uplink transmission resources allocated to the sensing node.

[0022] In conjunction with the second aspect, in one possible implementation, the network device associates sensing data of different data modalities based on the sixth information.

[0023] In conjunction with the second aspect, in one possible implementation, the network device performs fusion calculations based on sensing data from multiple data modalities to obtain sensing results; and sends the sensing results to the core network.

[0024] In conjunction with the second aspect, in another possible implementation, the network device sends the processed sensing data from the sensing nodes to the core network; the processed sensing data is then used by the core network to obtain the sensing results.

[0025] 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.

[0026] Thirdly, a communication device is provided, comprising a processing module and a transceiver module. The processing module is used to collect sensing data; the transceiver module is used to send a first message to a network device; wherein the sensing node information includes at least one of first information, second information, and third information, and the sensing data information includes at least one of fourth information, fifth information, and sixth information. The first information indicates the communication cost of the sensing node, the second information indicates the computing resource status of the sensing node, the third information indicates the energy status of the sensing node, the fourth information indicates the data mode of the sensing data, the fifth information indicates the data volume of the sensing data, and the sixth information indicates the acquisition time of the sensing data.

[0027] Fourthly, a communication device is provided, comprising a processing module and a transceiver module. The transceiver module is used to send a sensing task to a sensing node; the sensing task is used to trigger the sensing node to collect sensing data. The transceiver module is also used to receive a first message from the sensing node; wherein the sensing node information includes at least one of first information, second information, and third information, and the sensing data information includes at least one of fourth information, fifth information, and sixth information. The first information indicates the communication cost of the sensing node, the second information indicates the computing resource status of the sensing node, the third information indicates the energy status of the sensing node, the fourth information indicates the data mode of the sensing data, the fifth information indicates the data volume of the sensing data, and the sixth information indicates the acquisition time of the sensing data.

[0028] 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.

[0029] 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.

[0030] In one implementation, the communication interface may be a transceiver, or an input / output interface.

[0031] 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.

[0032] 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.

[0033] In one implementation, the communication interface may be a transceiver, or an input / output interface.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] Optionally, the processor may be one or more, and the memory may be one or more.

[0038] 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.

[0039] 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.

[0040] Eleventhly, a communication system is provided, including the aforementioned terminal device and network device. Optionally, the communication system may further include other devices that communicate with the terminal device and / or network device. Attached Figure Description

[0041] Figure 1 A schematic diagram of a communication system to which the communication method provided in the embodiments of this application is applicable;

[0042] Figure 2 A schematic diagram illustrating a communication method provided in an embodiment of this application;

[0043] Figure 3 A schematic block diagram of a communication device provided in the embodiments of this application;

[0044] Figure 4 Another schematic block diagram of the communication device provided in the embodiments of this application. Detailed Implementation

[0045] In the description of the embodiments of this application, the terminology used in the following embodiments is for the purpose of describing specific embodiments only and is not intended to be a limitation of this application. As used in the specification and appended claims of this application, the singular expressions "a," "the," "the," "the," and "this" are intended to also include expressions such as "one or more," unless the context clearly indicates otherwise. It should also be understood that in the following embodiments of this application, "at least one" and "one or more" refer to one or more (including two). The term "and / or" is used to describe the relationship between related objects, indicating that three relationships can exist; for example, A and / or B can indicate: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship.

[0046] References to "one embodiment" or "some embodiments" in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized. The term "connection" includes direct connections and indirect connections, unless otherwise stated. "First" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated.

[0047] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0048] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0049] The technical solutions provided in this application can be applied to various communication systems, such as: 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), 6G mobile communication systems, etc. Among them, 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 this application.

[0050] Figure 1 This is a schematic diagram of a communication system to which the communication method provided in the embodiments of this application is applicable.

[0051] It should be noted that the system architecture and business scenarios described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0052] like Figure 1 As shown, the communication system 100 may include a core network (CN), access network equipment, terminal equipment, etc.

[0053] Terminal devices can be devices that provide voice and / or data connectivity to users, handheld devices with wireless connectivity, or other processing devices connected to a wireless modem. Terminal devices can also be called terminals, user equipment (UE), mobile stations, mobile terminals, 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. Terminal devices can be mobile phones, tablets, computers with wireless transceiver capabilities, wearable devices, vehicles, aircraft (such as drones, helicopters, and airplanes), hot air balloons, ships, robots, robotic arms, or smart home devices, etc. In some possible implementations, the terminal device can also be a device with data sensing and data acquisition functions; such as sensor devices, roadside equipment, cameras, radar, etc. The embodiments of this application do not limit the form of the terminal device.

[0054] Access network equipment, sometimes also called access nodes, has wireless transceiver capabilities for communicating with terminal devices. 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 in open RAN (ORAN) systems, satellites in NTN communication systems, or base stations in future mobile communication 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, micro base station, indoor station, relay node, or a wireless controller in a cloud radio access network (CRAN) scenario. The embodiments of this application do not limit the specific technologies or equipment forms used in the access network equipment. In this application, access network equipment is referred to as network equipment.

[0055] The Network Access Center (CN) is a core component of a communication network, responsible for data transmission, routing, switching, and network control. The CN interacts with terminal devices through access network equipment (such as base stations) to perform functions such as user authentication, session management, data forwarding, and mobility management. In some embodiments, the CN also has the capability to process data, such as receiving, processing, and obtaining computational results from sensed data.

[0056] Taking 5G networks as an example, the 5G core network can also be called 5GC (5G core). For example... Figure 1 As shown, the 5GC may include entities such as the access and mobility management function (AMF), session management function (SMF), user plane function (UPF), unified data management (UDM), application function (AF), network exposure function (NEF), policy control function (PCF), and data transfer function (DTF).

[0057] It should be noted that entities in 5GC can also be referred to as network elements, devices, etc. Understandably, 5GC can also include more or fewer entities, such as mobility management entity (MME) entities, serving gateway (SGW) entities, packet data network gateway (PGW) entities, etc.

[0058] The definitions and functions of each entity in 5GC can be referenced from the 3rd Generation Partnership Project (3GPP) protocol, and will not be repeated in this embodiment. In one possible design, the DTF entity is used to initiate sensing services, transmit sensing data, and process the sensing data to obtain calculation results. For example, refer to... Figure 1When a terminal device has the ability to acquire sensing data, the DTF entity can select a terminal device as a sensing node and instruct the terminal device to establish a sensing service, also known as a sensing task or sensing service. After the terminal device establishes a sensing task, it collects sensing data and transmits the sensing data to the CN through the access network equipment. The DTF entity of the CN processes the sensing data and obtains the calculation results based on the sensing data.

[0059] The aforementioned CN and access network equipment belong to the 3GPP network. Optionally, the communication system 100 may also include non-3GPP networks, such as the Internet, wireless local area networks (WLANs), etc. For example, the UPF entity in the CN can forward data to or receive data from a non-3GPP network. Optionally, the communication system 100 may also include a data network (DN). The DN is used to store, transmit, exchange, and process data information, supporting various forms of information interaction such as text, images, video, and audio. For example, the DN can be implemented by a server. Terminal devices can read data information from the DN or send data information to the DN through the 3GPP network.

[0060] It should be noted that the names of the various devices, apparatuses, or entities mentioned above may change as technology advances. For example, the DTF entity mentioned above may also be called a data plane function (DPF) entity, a data processing entity, etc.

[0061] It should be noted that, Figure 1 Various devices, apparatuses, or entities may communicate directly or through forwarding from other devices, apparatuses, or entities. This application does not specifically limit this. For example, a terminal device can communicate directly with an AMF entity or through forwarding from an access network device. Similarly, an AF entity can communicate directly with an SMF entity or through forwarding from a NEF entity. This is explained uniformly here and will not be elaborated further below.

[0062] 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 (e.g., a UE) serving as the apparatus for implementing the functions of the terminal device is described.

[0063] 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.

[0064] Access network equipment and / or terminal equipment 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 equipment and terminal equipment. They can be deployed in the same or different scenarios; for example, both can be deployed on land simultaneously; or the access network equipment can be deployed on land while the terminal equipment is deployed on water, etc., and so on.

[0065] 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).

[0066] 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.

[0067] 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.

[0068] 1. Perception Task

[0069] A perception service consumer can request the establishment of a perception task from the DPF entity in the CN (Network Provider Interface). This perception service consumer can be a third-party service platform such as an autonomous driving platform, industrial monitoring system, smart city management platform, or environmental monitoring system, or it can be a network element or entity within the 3GPP network. The DPF entity selects a perception node based on the requirements of the perception task to collect perception data. In one possible implementation, a terminal device can act as a perception node. The DPF entity selects a terminal device as a perception node and sends a perception task request to that terminal device, carrying configuration parameters such as perception duration, sampling frequency, and data encoding method. The terminal device can then collect various perception data through its sensors according to the configuration parameters.

[0070] On the network side, a data transmission channel for sensing data is also established, through which terminal devices can report sensing data to the DPF entity. Furthermore, the DPF entity can calculate sensing results based on the sensing data, such as speed and angle, and report these results to the sensing service consumer.

[0071] 2. Data Mode

[0072] Data modality refers to the data type of sensed data in a wide-area heterogeneous sensor network. In one example, this includes visual modality, audio modality, text modality, sensor modality, etc.; for instance, visual modality sensed data includes image data, video data, infrared image data, depth image data, point cloud data, etc.; audio modality sensed data includes speech data, ambient sound data, ultrasonic data, vibration audio data, etc.; and sensor modality sensed data includes radar data, temperature and humidity data, air pressure data, acceleration data, etc.

[0073] 3. Local preprocessing

[0074] Sensing nodes can process the sensed data by compression and feature extraction before transmitting it to the network. This process of compression and feature extraction is called local preprocessing. By performing local preprocessing before transmitting the preprocessed data, the key information of the data can be preserved, providing high-quality input for subsequent processing on the network side, while reducing the transmission burden.

[0075] 4. Communication costs

[0076] Communication costs reflect the difficulty and resource consumption of sensing nodes in wireless transmission. In one example, communication costs include channel quality metrics such as reference signal receiving power (RSRP) and signal-to-interference-plus-noise ratio (SINR); communication costs can also include data transmission quality metrics such as latency and bit error rate.

[0077] 5. Computational resource status

[0078] This reflects the ability of the sensing node to perform local preprocessing. In one example, the computing resource status includes central processing unit (CPU) utilization, graphics processing unit (GPU) utilization, available memory, etc.

[0079] 6. Energy State

[0080] This reflects the energy status of the sensing node. In one example, the energy status includes the amount of electricity.

[0081] 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.

[0082] Currently, terminal devices typically use the buffer state report (BSR) mode to transmit data to the network. However, in wide-area heterogeneous sensor networks, when terminal devices act as sensing nodes, there are certain drawbacks to using BSR to transmit sensing data to network devices. BSR relies on the amount of data to be transmitted and the channel quality reported by the terminal device to the network device. The network device determines the data transmission behavior of the terminal device based on the amount of data to be transmitted and the channel quality, ignoring factors such as the terminal device's local computing capabilities, current CPU load, and battery status. This results in incomplete scheduling decision information and a lack of global optimization guidance from the network side, making it unable to adapt to the differences in computing power, energy consumption, and mobility among different types of sensing nodes in wide-area heterogeneous sensor networks. Furthermore, in BSR, the terminal device uniformly transmits raw data, ignoring its processing capabilities, leading to redundant data transmission, wasted wireless bandwidth, and a lack of flexible adjustment of transmission strategies based on different data characteristics, resulting in policy rigidity. Moreover, in wide-area heterogeneous sensor networks, the sensed data is highly differentiated in terms of real-time performance, importance, and energy consumption sensitivity. The traditional rigid mode of BSR, which treats all terminal devices the same and adopts a "one-size-fits-all" approach, cannot achieve on-demand adaptive optimization and is difficult to simultaneously meet the requirements of target detection accuracy and sustainable operation in resource-constrained environments.

[0083] In view of this, embodiments of this application provide a communication method that can dynamically realize local preprocessing and allocation of corresponding transmission resources for terminal devices in a highly heterogeneous and resource-constrained wide-area heterogeneous sensor network based on the communication cost, computing resource status, energy cost, and data mode of the terminal device, thereby ensuring the quality of critical data, improving data transmission efficiency, and reducing resource overhead.

[0084] In some embodiments, before transmitting sensing data to the network device as a sensing node, the terminal device sends the sensing node's communication cost, computing resource status, energy status, etc., to the network device. This allows the network device to allocate appropriate transmission resources to the sensing node based on its specific circumstances and to decide whether the sensing node should perform local preprocessing.

[0085] In one implementation, the network device can allocate corresponding uplink transmission resources to the sensing nodes based on their communication costs. These resources could include resource blocks (RBs) and modulation and coding schemes (MCS). For example, for sensing nodes with poor channel quality, the network device might allocate more robust modulation and coding schemes or more resource blocks to improve the reliable transmission of data reported by the sensing node. Conversely, for sensing nodes with good channel quality, higher-order modulation might be used to improve spectral efficiency.

[0086] In one implementation, the network device can determine the computing power of a sensing node based on its computing resource status, and then decide whether to perform local preprocessing based on that computing power. For example, for sensing nodes with low computing load (low CPU or GPU utilization) and sufficient available memory, the network device can instruct them to perform more complex feature extraction or artificial intelligence (AI) model inference; while for sensing nodes with limited computing resources (high CPU or GPU utilization), the network device can instruct them to perform simpler data compression or directly upload the raw data, thus shifting the consumption of computing resources to the network side.

[0087] In one implementation, the network device can determine the energy status of a sensing node based on its energy condition and decide whether it should perform local preprocessing. For example, for a sensing node with good energy (sufficient power), the network device can instruct it to perform more energy-intensive local preprocessing, such as feature extraction; for a sensing node with poor energy (low power), the network device can instruct it to perform less energy-intensive local preprocessing, such as light data compression or directly uploading the raw data, to prioritize ensuring the sensing node's continuous network connectivity.

[0088] In some embodiments, before transmitting sensing data to the network device as a sensing node, the terminal device sends the sensing data information to be transmitted, such as data modality, data volume, and acquisition time, to the network device. This facilitates the network device in allocating appropriate transmission resources to the sensing node based on the specific characteristics of the sensing data, and in deciding whether the sensing node should perform local preprocessing.

[0089] In one implementation, the network device allocates appropriate uplink transmission resources to sensing nodes based on the data modality (e.g., image, audio, text, sensor data) and data volume. It can also decide whether the sensing node should perform local preprocessing. For example, for images or videos with large data volumes, local preprocessing by the sensing node is prioritized, such as data compression or feature extraction. For sensor data with smaller data volumes but high value density (e.g., alarm signals), the sensing node does not need local preprocessing and uploads directly. Furthermore, different feature extraction algorithms are assigned to sensing data with different data modalities, such as images and audio.

[0090] In one implementation, network devices can perform data fusion based on the acquisition time (e.g., timestamp) of the sensed data, combining sensed data from different data modalities reported by the same sensed node or from different sensed nodes. For example, merging sensed data from the same moment; or combining sensed data from the same moment for calculation and decision-making. For instance, a network device can combine visual modal sensed data from a camera and audio modal sensed data from a microphone to comprehensively judge an event; or it can combine sensed data from multiple sensed nodes for triangulation. During data fusion, the acquisition time of the sensed data can serve as a reliable basis for data alignment and synchronization. Network devices use the timestamps of the sensed data to associate different sensed data generated at the same moment to ensure the accuracy of the fusion result. For example, fusing image features and audio features from the same moment is necessary to accurately determine the correspondence between the sound source location and the visual target. In this implementation, the sensing nodes report the collection time of the sensing data, and the network devices can establish the correlation between different sensing data through the collection time. This facilitates the fusion and optimization of sensing data of multiple data modalities and the resource coordination across sensing nodes, and improves the problem of low overall network efficiency caused by each sensing node responding independently to the network device scheduling.

[0091] In one implementation, network devices can determine the timeliness of sensing data based on its acquisition time (e.g., timestamp), thereby determining the validity of the sensing data. For example, if it's necessary to predict future temperature trends based on temperature data from the past three days, then data acquired more than three days ago would be deemed invalid. This ensures the real-time nature and accuracy of high-value sensing data.

[0092] Based on information about the sensing nodes (e.g., communication costs, computing resource status, energy status, etc.) and / or information about the sensing data (e.g., data modality, data volume, acquisition time, etc.), the network device allocates corresponding uplink transmission resources to the sensing nodes and determines the data processing method (e.g., whether local preprocessing is required). The terminal device (sensing node) then processes the sensing data according to the corresponding data processing method and sends the raw or locally preprocessed sensing data to the network device using the appropriate uplink transmission resources. Furthermore, the network device can fuse sensing data from multiple data modalities and calculate the sensing results. Alternatively, the network device can upload sensing data from multiple data modalities to the CN (Network Controller), where the CN fuses the sensing data from multiple data modalities and calculates the sensing results. The network device and the CN work in a layered collaboration to achieve efficient and flexible resource scheduling and management.

[0093] 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.

[0094] 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.

[0095] It should be noted that in the embodiments of this application, when the first network element sends a message (or signaling, data packet, information, etc.) to the second network element, it can be that the first network element sends the message directly to the second network element, or the first network element forwards the message to the second network element through a third network element.

[0096] Figure 2 This is a schematic diagram of a communication method provided in 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 includes the following steps:

[0097] S201 and CN establish a global awareness task.

[0098] The CN (Center for Perception) establishes a global perception task based on the request from the perception service consumer. This global perception task is macroscopic and goal-oriented. In one implementation, the CN generates the task type, task parameters, etc., for this global perception task. In one example, the task type is related to the type of perception service consumer, such as autonomous driving on urban roads, security inspection of industrial parks, urban traffic flow monitoring, and regional air quality assessment. Task parameters include the geographical coverage area, the type of perception data (e.g., images, sound, temperature, humidity), data update frequency, task validity period, task priority, and quality of service (QoS) requirements (e.g., latency limits, reliability levels).

[0099] S202 and CN send a global awareness task to the network device. Correspondingly, the network device receives the global awareness task.

[0100] In one implementation, the CN determines one or more network devices based on the geographical coverage of the global awareness task and sends the global awareness task to those devices. Specifically, the CN encapsulates the global awareness task into a global awareness task instruction through a standard interface (such as the NG interface) and sends it to the one or more network devices.

[0101] In one example, the global awareness task instruction includes metadata such as task ID, task type, target area, data modality, data reporting cycle, and task validity period. Optionally, the global awareness task instruction may also include task priority and task constraints, such as QoS requirements, maximum energy consumption budget, and data privacy level. The task ID uniquely identifies a global awareness task, the target area is determined based on the geographical coverage, the data modality is determined based on the type of awareness data, and the data reporting cycle is determined based on the data update frequency. It is understood that a global awareness task may include one or more data modalities.

[0102] S203. The network device generates a perception task within its coverage area based on the global perception task.

[0103] Network devices perform task parsing for global awareness tasks, identifying task objectives, data modalities to be collected, coverage areas, data reporting cycles, task validity periods, task priorities, QoS requirements, maximum power consumption budgets, and data privacy levels. In one example, the network device determines the task objective based on the task type in the global awareness task instruction, determines the data modalities that the network device needs to collect based on the data modalities in the global awareness task instruction, and determines the coverage area based on the target area in the global awareness task instruction.

[0104] Furthermore, the network device decomposes the task based on the task objectives, the data modes to be collected, the coverage area, etc., and decomposes the global perception task into one or more perception tasks associated with the network device.

[0105] In one example, the network device determines the associated sensing nodes based on the coverage of the global sensing task and the deployment of sensing nodes within the coverage area of ​​the network device; and determines the specific information of each sensing task based on the data modalities to be collected and the capability profiles of the associated sensing nodes, such as node type, sensor type, computing power, etc.

[0106] In one implementation, a sensing task includes a subtask ID, a task ID, node information, task instructions, data reporting conditions, and resource information. The subtask ID uniquely identifies a sensing task; the task ID identifies the global sensing tasks associated with the sensing task; the node information indicates one or more sensing nodes associated with the sensing task; the task instructions describe the sensing task, such as acquiring images and extracting vehicle information, or acquiring temperature data; the data reporting conditions indicate the reporting conditions for sensing data, such as event triggering or periodic reporting; and the resource information indicates the resource usage rules related to the sensing task, such as maximum CPU utilization and maximum energy consumption thresholds.

[0107] S204. The network device sends a sensing task to the sensing node. Correspondingly, the sensing node receives the sensing task.

[0108] In one implementation, the network device sends a sensing task to one or more sensing nodes associated with the sensing task via downlink control signaling, such as a radio resource control (RRC) reconfiguration message. For example, this downlink control signaling includes sensing task information (e.g., task ID, task instructions, data reporting conditions, resource information, etc.), sensing data reporting format, and status reporting rules (e.g., periodic reporting, event-triggered reporting, task initialization-triggered reporting, etc.).

[0109] S205, Perception Node Analysis and Confirmation of Perception Task.

[0110] The sensing node parses the sensing task and configures its local sensors and computing resources accordingly. In one implementation, if the sensing node's local configuration is successful, it sends a task confirmation signal, such as an RRC Reconfiguration Complete message, to the network device to indicate that the sensing node is ready to execute the sensing task. If the sensing node's local configuration fails, for example due to insufficient computing resources or sensor malfunction, it sends a task failure signal to the network device; optionally, it can also report the reason for the failure.

[0111] S206. The sensing node collects sensing data and determines the data mode, data volume, etc. of the sensing data.

[0112] When a sensing node includes multiple sensors, it can collect sensing data in multiple data modalities, such as images and audio.

[0113] S207, The sensing node triggers the reporting of status information.

[0114] After completing the initial collection of sensing data and data modality identification, the sensing node can trigger the reporting of status information to the network device. For example, the status information includes sensing node information and sensing data information. The sensing node information includes the sensing node's communication cost, computing resource status, energy status, etc., while the sensing data information includes data modality, data volume, collection time, etc.

[0115] In one possible implementation, the sensing node triggers the reporting of status information according to the status reporting rules of the sensing task.

[0116] In one example, the status reporting rule is that reporting is triggered upon task initialization. Accordingly, when the sensing node first receives the sensing task from the network device, it reports the status information to the network device.

[0117] In another example, the status reporting rule is periodic reporting. Accordingly, the sensing nodes report status information according to a preset period (e.g., every 5 seconds). This allows network devices to obtain the latest status information.

[0118] In another example, the status reporting rule is event-triggered reporting. Accordingly, when a sensing node detects that an event trigger condition is met, it reports status information. For example, the event trigger condition could be that the change in the sensing node's communication cost, computing resource status, or energy status exceeds a preset value; for example, the battery level is below a preset battery threshold; another example is that the CPU load increase exceeds a preset load change threshold; yet another example is that the channel quality index decreases beyond a preset quality change threshold. For example, the event trigger condition could be that the sensing node has collected high-priority sensing data; for example, high-priority sensing data includes anomaly detection data, alarm event data, etc. For example, the event trigger condition could be that the sensing node completes a local preprocessing operation, or before the sensing node prepares to perform the next local preprocessing operation.

[0119] In another possible implementation, the sensing node triggers the reporting of status information in response to receiving a specific instruction from the network device. In one example, the network device can issue a specific RRC reconfiguration instruction, requesting the sensing node to report status information. For instance, in a sensing task reconfiguration or network optimization scenario, the network device proactively sends a specific RRC reconfiguration instruction to the sensing node. When the sensing node receives the specific RRC reconfiguration instruction, it reports the status information.

[0120] In addition to reporting data volume to network devices, sensing nodes proactively report their own status (e.g., computing resource status, energy status), data modality, and data acquisition time. This breaks the limitations of traditional BSR models that rely solely on data volume for scheduling. Network devices obtain the global information needed for decision-making, laying a data foundation for more intelligent and refined decisions. This enables the communication system to identify differences between sensing nodes in different situations (e.g., nodes with low or sufficient energy) and between different data (e.g., critical alarm data or routine monitoring data), providing the possibility for on-demand scheduling in subsequent processes.

[0121] S208. The sensing node sends status information to the network device. Correspondingly, the network device receives the status information.

[0122] In one implementation, the sensing node sends status information to the network device via RRC signaling, such as the ULInformationTransfer message.

[0123] In one specific implementation, the sensing node sends sensing node information and / or sensing data information to the network device; wherein, the sensing node information includes at least one of communication cost, computing resource status and energy status, and the sensing data information includes at least one of data mode, data volume and acquisition time.

[0124] For example, the structure of the ULInformationTransfer message is as follows:

[0125] ULInformationTransfer Message{

[0126] rrc-TransactionIdentifier:3 / / Transaction identifier

[0127] criticalExensions:ulInformationTransfer{

[0128] dedicatedInfoType:dedicatedinfoNAS{

[0129] OCTET STRING / / Carries serialization state information

[0130] Serialized content:

[0131] ProtocolHeader { (1 byte)

[0132] Version: 0x01

[0133] MessageType: 0xA1 / / Status information reporting

[0134] }

[0135] SensingDataReport{ / / Sensing data information

[0136] timestamp(INTEGER) / / Data collection time

[0137] dataModalities(BIT STRING) / / Data Modal Set

[0138] Data Mode 1

[0139] Data Mode 2

[0140] Data Mode 3

[0141] ...

[0142] dataVolume(INTEGER) / / Data volume (bytes)

[0143] }

[0144] CostReport { / / Sense node information

[0145] communicationCost (INTEGER) / / Communication Cost

[0146] computationCost(INTEGER) / / Computation resource status

[0147] energyCost(INTEGER) / / Energy state

[0148] }

[0149] }

[0150] }

[0151] }

[0152] In this example, the dedicatedinfoNAS structure in the ulInformationTransfer of the ULInformationTransfer message includes SensingDataReport and / or CostReport, where SensingDataReport is used to carry sensing data information and CostReport is used to carry sensing node information.

[0153] Understandably, the ULInformationTransfer message structure described above is merely an example and does not define the message structure for sensing nodes to send status information. For instance, in the ULInformationTransfer message structure, SensingDataReport includes a timestamp (acquisition time), dataModalities (data modality set), and dataVolume (data volume). The timestamp corresponds to the data modality set, indicating the acquisition time of sensing data from multiple data modalities, and the dataVolume corresponds to the data modality set, indicating the total data volume of sensing data from multiple data modalities. In other examples, each data modality may correspond to a separate timestamp (acquisition time) to indicate the acquisition time of sensing data for that data modality; and each data modality may correspond to a separate dataVolume (data volume) to indicate the data volume of sensing data for that data modality.

[0154] S209. The network device allocates data processing methods to the sensing node based on the status information, and allocates uplink transmission resources for the sensing data from the sensing node based on the status information.

[0155] In one implementation, the network device includes an AI module that allocates corresponding uplink transmission resources and data processing methods to the sensing nodes.

[0156] For example, the AI ​​module can dynamically allocate corresponding uplink transmission resources to sensing nodes based on their communication costs, data modalities and volumes of sensed data, and QoS requirements of the sensing task, achieving adaptive joint allocation of MCS and RB. Uplink transmission resources include MCS and RB. In one implementation, the AI ​​module first performs initial resource estimation. Specifically, based on the amount of data to be uploaded by the sensing node and the QoS parameters of the task, such as the target block error rate (BLER), combined with historical data transmission statistics, the AI ​​module initially estimates the number of RBs required to complete the current sensed data transmission. Then, the AI ​​module performs channel quality mapping and MCS selection. Specifically, the AI ​​module maps RSRP, SINR, etc., from the communication costs reported by the sensing node to a channel quality indicator (CQI). Based on this CQI and the current cell interference level, it selects an optimal MCS (MCS_selected) from a predefined MCS table. For example, while meeting the target BLER, it selects a higher-order MCS with higher spectral efficiency. Next, the AI ​​module performs fine-tuning of the number of RBs. Specifically, after selecting the MCS, the AI ​​module performs refined calculations. For example, based on the data volume and the number of bits per RB corresponding to the selected MCS, it accurately calculates the minimum number of RBs required (RB_theory). The AI ​​model introduces a dynamic compensation factor α (e.g., 0.8 ≤ α ≤ 1.5), which is jointly determined by the stability of SINR, node mobility prediction, and historical retransmission rate. For sensing nodes with unstable channels or in motion, α > 1, so additional RBs are allocated for redundancy protection; for sensing nodes with relatively stable channels and stationary locations, α can be slightly less than 1 to improve spectrum utilization. The final number of resource blocks allocated (RB_final) is determined by the formula RB_final = ceil(RB_theory * α), where ceil is a rounding function to ensure sufficient resources. The AI ​​model further performs time-frequency resource location scheduling. Specifically, specific physical resource blocks are allocated to sensing nodes in the time-frequency resource grid scheduled by network devices. When allocating physical resource blocks, both frequency domain resources and time domain resources are considered. In the frequency domain, based on SINR measurements, subbands with better channel conditions are prioritized for allocation; alternatively, frequency diversity techniques are employed to distribute resources across different subbands to combat frequency-selective fading. In the time domain, scheduling is performed in the nearest or most suitable uplink time slot based on the task's latency requirements and the urgency of the data. Finally, the AI ​​model encapsulates the above decision results into standard uplink grant information.Specifically, the allocated time-domain resources in the uplink grant information include start symbols, time slot indexes, etc.; the allocated frequency-domain resources include start resource blocks and the length of continuously allocated resource blocks (i.e., RB_final); the allocated MSCs include the selected MCSs (MCS_selected). Optionally, the uplink grant information may also include power control commands, demodulation reference signal (DMRS) configurations, etc.

[0157] For example, network devices can assign data processing methods to sensing nodes based on their computing resource status, energy status, data modality and volume of the sensing data, and task instructions for the sensing task. Data processing methods include feature extraction, compressed uploading, raw data uploading, data sampling or downsampling, and structured event description. Feature extraction means that the sensing node extracts key features from the sensing data and uploads these features to the network device; this method is suitable for high-dimensional, high-value data modalities such as images and audio. Compressed uploading means that the sensing node compresses the sensing data, discarding redundant information, before uploading it to the network device; this method is suitable for periodic or low-rate-of-change sensing data and can be determined based on the status reporting rules of the sensing task or parameters such as the data modality of the sensing data. Raw data uploading means that the sensing node directly uploads the raw sensing data; this method is suitable for critical tasks or scenarios where sensing node resources (computing resource status, energy status, etc.) are sufficient. Data sampling or downsampling means that the sensing node reduces the frequency of sensing data collection or reporting; this method is suitable for scenarios monitoring slowly changing physical quantities and can be determined based on the task instructions for the sensing task. Structured event descriptions represent the process of converting raw sensing data into structured event reports before uploading them to network devices. This approach is suitable for scenarios with clear semantics and small data volumes, such as target detection, anomaly alarms, and state changes. In this embodiment, data processing methods such as feature extraction, compressed uploading, data sampling or downsampling, and structured event descriptions are referred to as local preprocessing of the sensing node.

[0158] The network device synthesizes the received information and dynamically selects the optimal data processing method for each sensing node, allocating corresponding uplink transmission resources. Specifically, the network device can identify data with high redundancy and low value, instructing sensing nodes to compress or filter it; for high-value data, it instructs sensing nodes to retain details or even upload the original data. This method effectively reduces the transmission of redundant data, conserving valuable wireless spectrum resources. For sensing nodes with limited energy, the network device can instruct them to use more aggressive data compression to reduce communication energy consumption, or allocate computing tasks to sensing nodes with sufficient energy, significantly extending the overall network's sustainable operating time. Binding resource allocation to data value and sensing node status avoids allocating high-quality resources to large amounts of redundant, low-value data, ensuring that valuable wireless resources are prioritized and fully utilized for the transmission of high-value, time-sensitive data.

[0159] S210, the network device sends data processing mode indication information and uplink transmission resource indication information to the sensing node. Correspondingly, the sensing node receives the data processing mode indication information and uplink transmission resource indication information.

[0160] In one implementation, the network device sends data processing mode indication information and uplink transmission resource indication information to the sensing node via RRC signaling, such as the RRCReconfiguration message. The data processing mode indication information indicates the data processing mode allocated to the sensing node, including feature extraction, compressed uploading, data sampling or downsampling, structured event description, and raw data uploading. The uplink transmission resource indication information indicates the uplink transmission resources allocated to the sensing node, including, for example, the start symbol in the time domain, the time slot index, the start resource block in the frequency domain, the length of the continuously allocated resource block, the selected MCS, power control commands, and DMRS configuration.

[0161] For example, the structure of the RRCReconfiguration message is as follows:

[0162] RRCReconfiguration Message{

[0163] rrc-TransactionIdentifier / / Transaction identifier used to match request and response messages.

[0164] criticalExtensions:Uplink resource allocation scheme{

[0165] c1{

[0166] rrcReconfiguration{

[0167] radioBearerConfig / / Wireless bearer configuration (creating, modifying, or releasing bearers)

[0168] measConfig / / Measurement configuration

[0169] nonCriticalExtension{ / / Optional extension to the signaling root

[0170] uplinkConfigDedicated{ / / Dedicated uplink transmission resource configuration container

[0171] uplinkGrant{ / / Uplink information

[0172] timeDomainAllocation{ / / Time domain resources

[0173] k2 / / Scheduling offset (slot index)

[0174] startSymbolAndLength / / Start symbol and length

[0175] }

[0176] frequencyDomainAllocation{ / / Frequency domain resources

[0177] resourceAllocationType / / Allocation type (RBG or contiguous RB)

[0178] rb-Start / / Starting RB index

[0179] rb-Num / / Length of the allocated RB

[0180] }

[0181] }

[0182] mcs / / MCS index

[0183] tp-pusch / / PUSCH power control command

[0184] }

[0185] dataPreprocessingConfig{ / / Configure the container for data processing methods

[0186] preprocessingMethod{ / / Data processing method

[0187] rawDataUpload / / Upload raw data

[0188] featureExtraction

[0189] dataCompression / / Compress upload

[0190] downSampling / / Downsampling

[0191] eventDescription / / Structured event description

[0192] }

[0193] }

[0194] }

[0195] }

[0196] }

[0197] }

[0198] }

[0199] In this example, `uplinkConfigDedicated` is a container indicating the uplink transport resource configuration, including information such as `timeDomainAllocation`, `frequencyDomainAllocation`, and `mcs`. `timeDomainAllocation` carries the time-domain resource allocation information for the uplink transport resources, `frequencyDomainAllocation` carries the frequency-domain resource allocation information for the uplink transport resources, and `mcs` indicates the selected MCS. `dataPreprocessingConfig` is a container indicating the data processing method configuration. `preprocessingMethod` indicates the data processing method; for example, `preprocessingMethod` can carry `rawDataUpload`, `featureExtraction`, `dataCompression`, `downSampling`, or `eventDescription`.

[0200] S211. The sensing node processes the sensing data according to the data processing method indicated by the data processing method instruction information.

[0201] In one example, the sensing node determines the corresponding data processing method based on the preprocessingMethod, calls the local processing program, and performs the corresponding preprocessing operations on the raw sensing data; such as extracting key feature vectors, compression, data sampling or downsampling, and converting into structured event descriptions.

[0202] S212. The sensing node uses the uplink transmission resource indicated by the uplink transmission resource indication information to send the locally preprocessed sensing data or raw sensing data to the network device. Correspondingly, the network device receives the locally preprocessed sensing data or raw sensing data.

[0203] In one example, the sensing node determines the uplink transmission resource based on uplinkConfigDedicated and sends sensing data to the network device on that uplink transmission resource; for example, sending key feature vectors, compressed data packets, or structured event descriptions to the network device; or, for example, sending raw sensing data to the network device.

[0204] Optionally, after receiving the locally pre-processed sensing data or the raw sensing data, the network device can choose either processing mode one or processing mode two. In processing mode one, the network device calculates the sensing result based on the sensing data (locally pre-processed sensing data or raw sensing data); accordingly, steps S213-S214 are executed. In processing mode two, the network device forwards the sensing data (locally pre-processed sensing data or raw sensing data) to the CN, and the CN calculates the sensing result based on the sensing data; accordingly, steps S215-S216 are executed.

[0205] Network devices can determine whether to select processing mode one or processing mode two based on the characteristics of the sensing task (such as real-time performance, coverage area, and computational complexity), network device load, and the resource status of sensing nodes (such as computing resource status and energy status). For example, if the sensing task requires extremely high real-time performance and involves strong data locality, processing mode one is selected to achieve local response and resource conservation for the network device. Conversely, if the sensing task requires wide-area data, complex global analysis, or core network computing power support, processing mode two is selected to achieve global processing.

[0206] S213. Network devices obtain sensing results based on sensing data.

[0207] In one implementation, the AI ​​module of the network device performs multimodal fusion of sensing data from one or more sensing nodes. In one example, the AI ​​module determines sensing data collected at the same time or related sensing data based on the acquisition time, facilitating the fusion of sensing data from different data modalities collected at the same time, or sensing data from different related data modalities. For example, image data acquired at the same time or with some correlation can be fused with audio data at an earlier stage. Furthermore, based on the multimodal sensing data, a multimodal deep learning model is used to perform complete fusion inference to arrive at a real-time decision conclusion, i.e., the sensing result.

[0208] S214. The network device sends the sensing results to the CN, or triggers local control commands based on the sensing results.

[0209] S215. The network device sends sensing data to the CN. Correspondingly, the CN receives the sensing data.

[0210] Network devices send either pre-processed sensing data or raw sensing data from the sensing nodes to the CN.

[0211] S216, CN obtains the perception results based on the perception data.

[0212] In one implementation, the CN's data fusion module performs multimodal fusion on sensing data from one or more network devices, makes a global decision, and obtains the sensing results.

[0213] Network devices or CNs utilize AI to fuse and analyze sensing data from different sensing nodes and of different types (multi-modal data) to extract deeper information. This overcomes the limitations of single-node or single-type sensing data, and through complementarity and verification, can significantly improve the accuracy and reliability of the final sensing results (such as target recognition and event detection).

[0214] The communication method provided in this application involves a sensing node extracting information such as the data modality and data volume of the sensing data, and uploading its own status (e.g., communication cost, computing resource status, energy status), sensing data characteristics (e.g., data volume, data modality), and the acquisition time of the sensing data to a network device. The network device allocates uplink transmission resources for the sensing node to transmit the sensing data, and dynamically determines the data processing method of the sensing node based on its communication cost, computing resource status, energy status, and the characteristics of the sensing data (e.g., data modality, data volume). This achieves a balance between the data volume and information value of the sensing data, improves the energy efficiency of the sensing node, extends the network lifespan, and adapts to the high reliability and low latency communication requirements of 6G. Furthermore, after processing the sensing data according to the corresponding data processing method, the sensing node sends the sensing data to the network device using the uplink transmission resources allocated by the network device. The network device or core network determines the correlation between sensing data of different data modalities based on the acquisition time of the sensing data uploaded by the sensing node, realizing multimodal fusion processing of the sensing data. This achieves resource management and scheduling for efficient acquisition, preprocessing, fusion, and uploading of multimodal sensing data.

[0215] It should be understood that Figure 2 The flowcharts 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... Figure 2 The examples in the document can be transformed into equivalent ways to obtain more implementations.

[0216] The above text combined Figure 2This document describes in detail the communication method provided in the embodiments of this application. The following will combine... Figures 3 to 4 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.

[0217] In the embodiments described above, the network device may execute some or all of the steps in each embodiment; the terminal device (sensing node) 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.

[0218] Figure 3 This is a schematic block diagram of a communication device provided in an embodiment of this application. Figure 3 As shown, the communication device 300 may include a processing module 310 and a communication module 320. The processing module 310 can implement corresponding processing functions. The communication module 320 can implement corresponding communication functions, which can be internal communication functions of the communication device 300 or communication functions between the communication device 300 and other devices. Optionally, the communication module 320 may also be referred to as a communication interface or a transceiver module.

[0219] Optionally, the communication device 300 further includes a storage module, which can be used to store instructions and / or data; the processing module 310 can read the instructions and / or data in the storage module so that the communication device 300 can implement the aforementioned method embodiments.

[0220] In one possible design, the communication device 300 may correspond to the sensing node in the above method embodiments, or to a component (such as a circuit, chip, or chip system) configured in the sensing node. The communication device 300 can be used to execute the steps or processes performed by the sensing node in any of the above method embodiments.

[0221] In one example, processing module 310 is used to collect sensing data.

[0222] The communication module 320 is used to send a first message to the network device; wherein the first message includes sensing node information and / or sensing data information, the sensing node information including at least one of first information, second information, and third information, and the sensing data information including at least one of fourth information, fifth information, and sixth information. Specifically, the first information indicates the communication cost of the sensing node, the second information indicates the computing resource status of the sensing node, the third information indicates the energy status of the sensing node, the fourth information indicates the data mode of the sensing data, the fifth information indicates the data volume of the sensing data, and the sixth information indicates the acquisition time of the sensing data.

[0223] Optionally, the communication module 320 is also used to receive a second message from the network device; the second message includes a first indication information and / or a second indication information, the first indication information being used to indicate the data processing method allocated to the sensing node, the data processing method including feature extraction, compressed uploading, data sampling or downsampling, structured event description, or raw data uploading, and the second indication information being used to indicate the uplink transmission resources allocated to the sensing node.

[0224] Among them, at least one of the second, third, fourth, and fifth information is used by the network device to allocate data processing methods to the sensing node. At least one of the first, fourth, and fifth information is used by the network device to allocate uplink transmission resources to the sensing node.

[0225] Optionally, the processing module 310 is further configured to process the sensed data according to the data processing method indicated by the first indication information. The communication module 320 is further configured to send the processed sensed data to the network device on the uplink transmission resources indicated by the second indication information.

[0226] Optionally, the sixth information is used by network devices to associate sensing data of different data modalities.

[0227] In another possible design, the communication device 300 may correspond to the network device in the above method embodiments, or to a component (such as a circuit, chip, or chip system) configured in the network device. The communication device 300 can be used to perform the steps or processes performed by the network device in any of the above method embodiments.

[0228] In one example, the communication module 320 is used to send a sensing task to the sensing node; the sensing task is used to trigger the sensing node to collect sensing data.

[0229] Optionally, the communication module 320 is further configured to receive a first message from the sensing node; wherein the first message includes sensing node information and / or sensing data information, the sensing node information including at least one of first information, second information, and third information, and the sensing data information including at least one of fourth information, fifth information, and sixth information. Specifically, the first information indicates the communication cost of the sensing node, the second information indicates the computing resource status of the sensing node, the third information indicates the energy status of the sensing node, the fourth information indicates the data mode of the sensing data, the fifth information indicates the data volume of the sensing data, and the sixth information indicates the acquisition time of the sensing data.

[0230] Optionally, the processing module 310 is used to assign a data processing method to the sensing node based on at least one of the second, third, fourth, and fifth information; wherein the data processing method includes feature extraction, compressed uploading, data sampling or downsampling, structured event description, or raw data uploading.

[0231] Optionally, the processing module 310 is also configured to allocate uplink transmission resources to the sensing node based on at least one of the first information, the fourth information, and the fifth information.

[0232] Optionally, the communication module 320 is also used to send a second message to the sensing node; the second message includes a first indication information and / or a second indication information, wherein the first indication information is used to indicate the data processing method allocated to the sensing node, and the second indication information is used to indicate the uplink transmission resources allocated to the sensing node.

[0233] Optionally, the processing module 310 is also used to associate the perceived data of different data modalities based on the sixth information.

[0234] Optionally, the processing module 310 is also used to perform fusion calculations based on the sensing data of multiple data modes to obtain sensing results; the communication module 320 is also used to send the sensing results to the core network.

[0235] Optionally, the communication module 320 is also used to send the processed sensing data of the sensing nodes to the core network; the processed sensing data is used by the core network to obtain sensing results.

[0236] The above are merely examples; for detailed steps or procedures, please refer to the descriptions in the foregoing embodiments.

[0237] Figure 4 This is another schematic block diagram of the communication device provided in the embodiments of this application. The communication device 400 may be a sensing node, or a chip, chip system, or processor within the sensing node that implements the above-described methods. The communication device 400 can be used to implement the methods described in the above method embodiments; for details, please refer to the descriptions in the above method embodiments.

[0238] like Figure 4 As shown, the communication device 400 may include one or more processors 410, which may also be referred to as processing units or processing modules, and can implement certain control functions. The processor 410 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 400 (e.g., a base station, baseband chip, user, user chip), execute software programs, and process data from the software programs.

[0239] In an alternative design, the processor 410 may also store instructions and / or data that can be executed by the processor 410 to cause the communication device 400 to perform the methods described in the above method embodiments.

[0240] In another alternative design, the communication device 400 may include a communication interface 420 for implementing receiving and transmitting functions. For example, the communication interface 420 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.

[0241] Optionally, the communication device 400 may include one or more memories 430, which may store instructions that can be executed on the processor 410, causing the communication device 400 to perform the methods described in the above method embodiments. Optionally, the memories 430 may also store data. Optionally, the processor 410 may also store instructions and / or data. The processor 410 and the memories 430 may be provided separately or integrated together.

[0242] 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.

[0243] In one implementation, the communication device 400 may correspond to the sensing node in the above method embodiments and may be used to execute the various steps and / or processes executed by the sensing node in the above method embodiments. The processor 410 may be used to execute instructions stored in the memory 430, and when the processor 410 executes the instructions stored in the memory, the processor 410 is used to execute the various steps and / or processes of the above method embodiments corresponding to the sensing node.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] The chip system may include input circuits or interfaces for transmitting information or data, and output circuits or interfaces for receiving information or data.

[0248] According to the method provided in the embodiments of this application, this application also provides a communication system, which includes the aforementioned sensing node, network device, CN, etc.

[0249] 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 performed by the sensing node in any of the foregoing method embodiments.

[0250] According to the method provided in the embodiments of this application, this application also provides a computer-readable storage medium storing program code, which, when run on a computer, causes the computer to execute the various steps or processes performed by the sensing node in any of the foregoing method embodiments.

[0251] 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.

[0252] 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.

[0253] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in 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.

[0254] 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.

[0255] 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.

[0256] 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, Applied to sensing nodes, the method includes: Collect sensor data; Send a first message to the network device; the first message includes sensing node information and / or sensing data information, wherein the sensing node information includes at least one of first information, second information and third information, and the sensing data information includes at least one of fourth information, fifth information and sixth information; Wherein, the first information indicates the communication cost of the sensing node, the second information indicates the computing resource status of the sensing node, the third information indicates the energy status of the sensing node, the fourth information indicates the data mode of the sensing data, the fifth information indicates the data volume of the sensing data, and the sixth information indicates the acquisition time of the sensing data; At least one of the second, third, fourth, and fifth information is used by the network device to allocate a data processing method to the sensing node. The data processing method includes feature extraction, compressed uploading, data sampling or downsampling, structured event description, or raw data uploading.

2. The method according to claim 1, characterized in that, The method further includes: Receive a second message from the network device; the second message includes first indication information, which is used to indicate the data processing method allocated to the sensing node.

3. The method according to claim 1, characterized in that, At least one of the first information, the fourth information, and the fifth information is used by the network device to allocate uplink transmission resources to the sensing node.

4. The method according to claim 3, characterized in that, The method further includes: A second message is received from the network device; the second message includes second indication information, which indicates uplink transmission resources allocated to the sensing node.

5. The method according to claim 2 or 4, characterized in that, The method further includes: The sensed data is processed according to the data processing method indicated by the first instruction information; On the uplink transmission resources indicated by the second indication information, the processed sensing data is sent to the network device.

6. The method according to any one of claims 1-4, characterized in that, The sixth piece of information is used by the network device to associate sensing data of different data modalities.

7. The method according to any one of claims 1-4, characterized in that, The communication cost includes at least one of the following: reference signal received power, signal-to-interference-plus-noise ratio, latency, and bit error rate; the computing resource status includes at least one of the following: CPU utilization, GPU utilization, and available memory; and the energy status includes battery power.

8. A communication method, characterized in that, Applied to network devices, the method includes: Send a sensing task to the sensing node; the sensing task is used to trigger the sensing node to collect sensing data. Receive a first message from the sensing node; the first message includes sensing node information and / or sensing data information, wherein the sensing node information includes at least one of first information, second information and third information, and the sensing data information includes at least one of fourth information, fifth information and sixth information; Wherein, the first information indicates the communication cost of the sensing node, the second information indicates the computing resource status of the sensing node, the third information indicates the energy status of the sensing node, the fourth information indicates the data mode of the sensing data, the fifth information indicates the data volume of the sensing data, and the sixth information indicates the acquisition time of the sensing data; Based on at least one of the second, third, fourth, and fifth information, a data processing method is assigned to the sensing node; the data processing method includes feature extraction, compressed uploading, data sampling or downsampling, structured event description, or raw data uploading.

9. The method according to claim 8, characterized in that, The method further includes: A second message is sent to the sensing node; the second message includes first indication information, which is used to indicate the data processing method allocated to the sensing node.

10. The method according to claim 8, characterized in that, The method further includes: Based on at least one of the first information, the fourth information, and the fifth information, uplink transmission resources are allocated to the sensing node.

11. The method according to claim 10, characterized in that, The method further includes: A second message is sent to the sensing node; the second message includes second indication information, which is used to indicate the uplink transmission resources allocated to the sensing node.

12. The method according to any one of claims 8-11, characterized in that, The method further includes: The sixth piece of information is used to associate perceived data of different data modalities.

13. The method according to any one of claims 8-11, characterized in that, The method further includes: The perception results are obtained by fusing and calculating the perception data from multiple data modalities. The sensing results are sent to the core network.

14. The method according to any one of claims 8-11, characterized in that, The method further includes: The processed sensing data from the sensing nodes is sent to the core network; the processed sensing data is used by the core network to obtain sensing results.

15. A communication device, characterized in that, The device includes at least one processor and a communication interface, the at least one processor and the communication interface being coupled to a memory storing a program or instructions, the processor executing the program or instructions to cause the communication device to perform the method as described in any one of claims 1-14.

16. 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 1-14.

17. A communication system, characterized in that, Includes the communication device as described in claim 15.

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