Perception prediction methods, devices, and computer storage media

Abstract

This application provides a sensing prediction method, device, and computer storage medium. The method includes: receiving indication information of a measurement configuration sent by a second device; the measurement configuration includes: a configuration for prediction and measurement; and sending first information to the second device, at least in part based on the indication information, wherein the first information is used to indicate prediction information. This application enables more rational resource utilization during sensing and communication, optimizing resource utilization efficiency.

Description

Technical Field

[0001] This application relates to the field of wireless communication, and more particularly to a sensing prediction method, device, and computer storage medium. Background Technology

[0002] The integration of sensing and communication technologies represents a development direction for wireless communication technology. However, the sensing process consumes resources originally intended for wireless communication. Therefore, when combining sensing and communication technologies, it is necessary to coordinate the resources required for both. Summary of the Invention

[0003] This application provides a perception prediction method, device, and computer storage medium that can coordinate the resources required for perception and communication.

[0004] In a first aspect, embodiments of this application provide a perception prediction method applied to a first device, comprising: receiving indication information of a measurement configuration sent by a second device; the measurement configuration including: a configuration for prediction and measurement; and sending first information to the second device, at least in part based on the indication information, the first information being used to indicate prediction information.

[0005] Using the above method, the first device can send prediction information to the second device, so that the second device can be configured to use prediction instead of measurement when the prediction information is relatively accurate, thereby reducing the resources consumed by measurement and enabling the sensing, measurement and communication processes to make coordinated use of limited resources.

[0006] In one implementation, the configuration of the prediction and measurement includes: time location information for the prediction and measurement.

[0007] Using the above method, the first device can determine the timing of prediction and measurement based on the predicted and measured time and location information.

[0008] In one implementation, the predicted and measured time location information includes at least one of a first time interval, a second time interval, a third time interval, and a fourth time interval.

[0009] By using the above method, multiple time intervals can be incorporated into the configuration of prediction and measurement, thereby enabling the first device to determine when the prediction and measurement will occur.

[0010] In one implementation, the first time interval is used to indicate the time interval of a prediction; the second time interval can be used to indicate the time interval between the x-th prediction in a set of at least one consecutive predictions and the x-th prediction in the next set of at least one consecutive predictions, where x is less than or equal to K, and K is the number of times the first time interval occurs within the second time interval; the third time interval can be used to indicate the time interval between two adjacent measurements within the second time interval; and the fourth time interval can be used to indicate the time interval between the y-th measurement in a set of at least one consecutive measurement and the y-th measurement in the next set of at least one consecutive measurements, where y is less than or equal to P, and P is the number of times the third time interval occurs within the fourth time interval.

[0011] The above method can determine the number of measurements and predictions, as well as the measurement cycle and prediction cycle in a set of measurements and predictions. It can also determine the total cycle of a set of measurements and predictions, which helps to unify the repetition method of measurements and predictions between the first and second devices.

[0012] In one implementation, the third time interval may be equal to the first time interval, and / or the fourth time interval may be equal to the second time interval.

[0013] The above methods enable measurement and prediction to exhibit more consistent regularity, making it easier for the first device to perform prediction and measurement.

[0014] In one implementation, the configuration of the prediction and measurement includes: the number of times the first time interval occurs within the second time interval.

[0015] Using the above method, the first device can know the number of times the first time interval occurs within the second time interval, thus enabling it to determine the predicted number of times more quickly.

[0016] In one implementation, the configuration of the prediction and measurement includes: a prediction timestamp within a second time interval, a prediction time interval within the second time interval, a predicted quantity, a measured quantity, an indication to enable or disable prediction, a measurement report sending time information, prediction auxiliary information, prediction error information, event-based prediction information, and an indication that the prediction timing and measurement timing are associated.

[0017] The transmission time information of the aforementioned measurement report may include: transmission time information of the measurement report carrying prediction information and / or transmission time information of the measurement report without prediction information. The transmission time of the measurement report carrying prediction information can be determined at least in part based on an offset δ relative to the transmission time of the measurement report without prediction information. For example, if the transmission time information of the measurement report is T1, then the transmission time of the measurement report carrying prediction information is T1+δ. The offset value can be determined based on prediction capability information. Through the above method, the first device can more accurately determine the timing of prediction implementation, the measurement parameters, whether prediction is needed, and the timing of measurement information transmission, thus implementing prediction and measurement more appropriately.

[0018] In one implementation, the associated prediction timing and measurement timing are used to indicate that the prediction timing and measurement timing overlap or partially overlap, or that the time interval is less than a configured threshold.

[0019] Using the above method, the first device can perform predictions and measurements within the configured threshold, and appropriately schedule predictions and measurements.

[0020] In one implementation, the information associated with the prediction timing and measurement timing is determined at least in part based on protocol predefined conditions, and the prediction and measurement configuration includes condition information of the protocol predefined conditions.

[0021] Using the above method, predefined conditions can be configured in the protocol, thereby determining the timing of prediction and measurement.

[0022] In one implementation, the information that triggers event-based prediction includes at least one of the following: whether to enable event-based prediction, a threshold value for the number of times an event occurs when making predictions based on events, the start time of monitoring the event, the duration of monitoring the event, a threshold for prediction error, and the event type.

[0023] Using the above method, the first device can make predictions based on event triggers, which helps to implement predictions when needed.

[0024] In one implementation, the perception prediction method includes: sending the prediction capability information of the measurement of the first device to the second device.

[0025] Using the above method, predictive capability information can be sent to the second device before receiving the measurement configuration from the second device, so that the second device can generate the measurement configuration based on the predictive capability information.

[0026] In one embodiment, the prediction capability information includes at least one of the following: indication information supporting prediction, information on supported prediction types, and time information for prediction processing.

[0027] Using the above method, the second device can generate a measurement configuration that is adapted to the prediction capability information of the first device based on whether the first device supports prediction, the type of prediction that can be obtained, and the prediction processing time, so as to better utilize the prediction capability of the second device.

[0028] In one implementation, the supported prediction type information includes at least the parameter types included in the measurement information.

[0029] Using the above method, the second device can generate a measurement configuration that is compatible with the first device, according to the parameter types included in the first device.

[0030] In one embodiment, the transmission time information of the measurement report includes: the offset of the transmission time of the measurement information carrying prediction information relative to the transmission time of the measurement information without prediction information, or the time offset from receiving the last symbol in the sensing signal to transmitting the measurement information; the auxiliary information for prediction includes: historical measurement information and / or information of the measured target; the information of the prediction error includes: the detection time window of the prediction error, and / or the observation offset value of the prediction error.

[0031] In this embodiment, the observation offset value of the prediction error refers to the offset of the measurement values ​​(including the measurement value of the current time) of M historical time points in a single prediction. Using the above method, the first device can specifically determine the timing of the operation performed within each cycle of prediction and measurement, or utilize the auxiliary information of the prediction to implement prediction more accurately.

[0032] In one embodiment, sending the first information to the second device includes: receiving a sensing signal; and based on the received sensing signal, sending the measurement information carrying the prediction information to the second device.

[0033] Using the above method, measurement information can be obtained based on the sensing signal, thus realizing sensing measurement.

[0034] In one implementation, the prediction information includes at least one of the following: prediction information and auxiliary prediction information.

[0035] Using the above method, the second device or the first device can more accurately determine the prediction error and optimize and adjust the relevant prediction parameters.

[0036] In one implementation, the prediction information includes: predicted values ​​of corresponding measurements at K future time points, where K is a positive integer.

[0037] The above method enables multiple consecutive predictions of the perceived target object, which helps to eliminate random factors and improve the accuracy of prediction.

[0038] In one implementation, the auxiliary prediction information includes at least one of the following: historical measurement values ​​of corresponding measurements over M historical time periods, and a motion model of the target being measured, where M is a positive integer.

[0039] Using the above methods, the first device can utilize more auxiliary information to make predictions, which helps to obtain more accurate prediction information.

[0040] In one embodiment, the perception prediction method further includes: the first information is also used to indicate measurement information corresponding to the prediction information.

[0041] The above method enables the simultaneous reporting of measurement and prediction information to the second device, facilitating the transmission of multiple pieces of information at once and reducing signaling overhead.

[0042] In one embodiment, the first device is a user equipment and the second device is a base station; or the first device is a base station and the second device is a sensing network element of the core network; or the first device is a user equipment and the second device is a sensing network element of the core network.

[0043] The above methods enable measurement and prediction to be carried out using a variety of devices, allowing communication systems to more fully integrate sensing technologies.

[0044] In a second aspect, a perception prediction method is applied to a second device, comprising: sending indication information of a measurement configuration to a first device; the measurement configuration including: a configuration for prediction and measurement; and receiving first information sent by the first device at least in part based on the indication information, the first information being used to indicate prediction information.

[0045] In one implementation, the time and location information is predicted and measured.

[0046] In one implementation, the configuration of the prediction and measurement includes at least one of a first time interval, a second time interval, a third time interval, and a fourth time interval.

[0047] In one implementation, the first time interval is used to indicate the time interval of a prediction; the second time interval is used to indicate the time interval between the x-th prediction in a set of at least one consecutive predictions and the x-th prediction in the next set of at least one consecutive predictions, where x is less than or equal to K, and K is the number of times the first time interval occurs within the second time interval; the third time interval is used to indicate the time interval between two adjacent measurements within the second time interval; and the fourth time interval is used to indicate the time interval between the y-th measurement in a set of at least one consecutive measurement and the y-th measurement in the next set of at least one consecutive measurements, where y is less than or equal to P, and P is the number of times the third time interval occurs within the fourth time interval.

[0048] In one implementation, the third time interval is equal to the first time interval, and / or the fourth time interval is equal to the second time interval.

[0049] In one implementation, the configuration of the prediction and measurement includes: the number of times the first time interval occurs within the second time interval.

[0050] In one implementation, the configuration of the prediction and measurement includes at least one of the following: a prediction timestamp within a second time interval, a prediction time interval within the second time interval, a predicted quantity, a measured quantity, an indication to enable or disable prediction, a measurement report sending time information, prediction auxiliary information, prediction error information, event-based prediction information, and an indication that the prediction timing and measurement timing are associated.

[0051] In one implementation, the associated prediction timing and measurement timing are used to indicate that the prediction timing and measurement timing overlap or partially overlap, or that the time interval is less than a configured threshold.

[0052] In one implementation, the information associated with the prediction timing and measurement timing is determined at least in part based on protocol predefined conditions, and the prediction and measurement configuration includes condition information of the protocol predefined conditions.

[0053] In one implementation, the protocol predefined conditions include: the time interval between the prediction timing and the measurement timing is less than or equal to a threshold value; the threshold value is dynamically configured via signaling or predefined by the protocol.

[0054] In one implementation, the information that triggers event-based prediction includes at least one of the following: whether to enable event-based prediction, a threshold value for the number of times an event occurs when making predictions based on events, the start time of monitoring the event, the duration of monitoring the event, a threshold for prediction error, and the event type.

[0055] In one embodiment, the perception prediction method further includes receiving prediction capability information measured by the first device.

[0056] In one embodiment, the prediction capability information includes at least one of the following: indication information supporting prediction, information on supported prediction types, and time information for prediction processing.

[0057] In one implementation, the supported prediction type information includes at least the parameter types included in the measurement information.

[0058] In one embodiment, the transmission time information of the measurement information includes: the offset of the transmission time of the measurement information carrying prediction information relative to the transmission time of the measurement information not carrying prediction information, or the time offset from receiving the last symbol in the sensing signal to transmitting the measurement information; the auxiliary information for prediction includes: historical measurement information and / or information of the measured target; the information of the prediction error includes: the detection time window of the prediction error, and / or the observation offset value of the prediction error.

[0059] In one embodiment, sending the first information to the second device includes: sending a received sensing signal; and the second device sending the measurement information carrying the prediction information to the second device based on the received sensing signal.

[0060] In one implementation, the prediction information includes at least one of the following: prediction information and auxiliary prediction information.

[0061] In one implementation, the prediction information includes: predicted values ​​of corresponding measurements at K future time points, where K is a positive integer.

[0062] In one implementation, the auxiliary prediction information includes at least one of the following: historical measurement values ​​of corresponding measurements over M historical time periods, and a motion model of the target being measured, where M is a positive integer.

[0063] In one embodiment, the perception prediction method further includes: the first information is also used to indicate measurement information corresponding to the prediction information.

[0064] In one embodiment, the first device is a user equipment and the second device is a base station; or the first device is a base station and the second device is a sensing network element of the core network; or the first device is a user equipment and the second device is a sensing network element of the core network.

[0065] Thirdly, embodiments of this application provide a sensing prediction method applied to a base station, comprising: receiving indication information of measurement configuration sent by a sensing network element; the measurement configuration includes: a configuration for prediction and measurement; and receiving prediction information sent by the sensing network element based on sensing information.

[0066] Fourthly, embodiments of this application provide a perception prediction method applied to a perception network element, comprising: sending indication information of measurement configuration to a base station; the measurement configuration including: a configuration of prediction and measurement; and sending prediction information based on perception information to the base station.

[0067] Fifthly, embodiments of this application provide a communication device that has the functions of implementing the first or second aspect described above. For example, the communication device includes modules or units that perform the operations involved in the first or second aspect described above. The modules or units can be implemented by software, or by hardware, or by hardware executing corresponding software.

[0068] In one possible design, the communication device includes a processing unit and a communication unit, wherein the communication unit can be used to transmit and receive signals to enable communication between the communication device and other devices; the processing unit can be used to perform some internal operations of the communication device. The functions performed by the processing unit and the communication unit may correspond to the operations involved in the first or second aspect described above. The processing unit may include a satellite communication processing unit.

[0069] In one possible design, the communication device includes a processor, which may include a satellite communication processor, and the processor may be coupled to a memory. The memory may store necessary computer programs or instructions for implementing the functions described in the first or second aspect above. The processor can execute the computer programs or instructions stored in the memory, and when the computer programs or instructions are executed, cause the communication device to implement the methods in any possible design or implementation of the first or second aspect above.

[0070] In one possible design, the communication device includes a processor and a memory, the memory of which may store necessary computer programs or instructions for implementing the functions described in the first or second aspect above. The processor may execute the computer programs or instructions stored in the memory, and when the computer programs or instructions are executed, cause the communication device to implement the methods in any possible design or implementation of the first or second aspect above.

[0071] In one possible design, the communication device includes a processor and an interface circuit, wherein the processor is configured to communicate with other devices via the interface circuit and to perform the methods in any possible design or implementation of the first or second aspect described above.

[0072] Understandably, in the fifth aspect above, the processor can be implemented in hardware or software. When implemented in hardware, the processor can be a logic circuit, integrated circuit, etc.; when implemented in software, the processor can be a general-purpose processor that reads software code stored in memory. Furthermore, there can be one or more processors, and one or more memories. The memory can be integrated with the processor, or the memory and processor can be separate. In specific implementations, the memory can be integrated with the processor on the same chip, or it can be set on different chips. This application does not limit the type of memory or the arrangement of the memory and processor.

[0073] Sixthly, embodiments of this application provide a non-terrestrial network communication system, including a transmitting end device and a receiving end device. The transmitting end device is used to implement the method for network devices provided in any embodiment of this application, and the receiving end device is used to implement the method for user equipment provided in any embodiment of this application.

[0074] In a seventh aspect, an embodiment of this application provides a communication device including a module for performing the methods provided in any embodiment of this application.

[0075] Eighthly, embodiments of this application provide a communication device, including one or more processors configured to perform the methods provided in any embodiment of this application.

[0076] Ninthly, embodiments of this application provide a chip system, including: a memory for storing a computer program; a processor; and when the processor retrieves and runs the computer program from the memory, a communication device equipped with the chip system executes the method provided in any embodiment of this application.

[0077] In a tenth aspect, embodiments of this application also provide a computer program product, the computer program product including instructions that, when executed on a processor, cause the processor to perform the method provided in any embodiment of this application.

[0078] Eleventhly, embodiments of this application provide a terminal device, including: a memory for storing computer programs; a processor; when the processor calls and runs the computer program from the memory, the terminal device executes the method provided in any embodiment of this application.

[0079] In a twelfth aspect, embodiments of this application also provide a computer-readable storage medium storing a computer program or instructions that, when executed by a communication device, implement the method provided in any embodiment of this application.

[0080] The technical effects brought about by the second to twelfth aspects above can be found in the description of the beneficial effects of the corresponding solutions in the first aspect above, and will not be repeated here. Attached Figure Description

[0081] Figure 1 A schematic diagram of the architecture of the communication system used in the embodiments of this application;

[0082] Figure 2 This is a schematic diagram of a scenario according to an embodiment of this application;

[0083] Figure 3A and Figure 3B This is a schematic diagram of another scenario according to an embodiment of this application;

[0084] Figure 4 This is a schematic diagram of a method flow according to an embodiment of this application;

[0085] Figure 5 This is a schematic diagram illustrating a prediction and measurement configuration according to an embodiment of this application;

[0086] Figure 6 This is a schematic diagram illustrating another prediction and measurement configuration according to an embodiment of this application;

[0087] Figure 7 This is a schematic diagram illustrating another prediction and measurement configuration according to an embodiment of this application;

[0088] Figure 8 This is a schematic diagram of another method flow according to an embodiment of this application;

[0089] Figure 9 This is a schematic diagram illustrating the method for determining the preprocessing time in an embodiment of this application;

[0090] Figure 10 This is a schematic diagram illustrating the prediction error calculation in an embodiment of this application;

[0091] Figure 11 This is a schematic diagram of another method flow according to an embodiment of this application;

[0092] Figure 12 This is a schematic diagram of another method flow according to an embodiment of this application;

[0093] Figure 13 This is a schematic diagram of another method flow according to an embodiment of this application;

[0094] Figure 14 This is a schematic diagram of another method flow according to an embodiment of this application;

[0095] Figure 15 This is a schematic diagram of a device structure according to an embodiment of this application;

[0096] Figure 16This is a schematic diagram of another device structure according to an embodiment of this application. Detailed Implementation

[0097] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings. This application will focus on various aspects, embodiments, or features of a system that may include multiple devices, components, modules, etc. It should be understood and appreciated that each system may include additional devices, components, modules, etc., and / or may not include all the devices, components, modules, etc. discussed in conjunction with the accompanying drawings. Furthermore, combinations of these solutions may also be used.

[0098] Furthermore, in the embodiments of this application, words such as "in one possible implementation," "exemplarily," "for example," "e.g.," "as," and "again" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as an "example" in this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of the term "example" is intended to present concepts in a concrete manner. In the embodiments of this application, "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably, and it should be noted that their intended meanings are consistent unless their distinction is emphasized.

[0099] The technical solutions in this application embodiment can be applied to various communication systems, such as Universal Mobile Telecommunications System (UMTS), Wireless Local Area Network (WLAN), Wireless Fidelity (Wi-Fi) system, 4th generation (4G) communication system, such as Long Term Evolution (LTE) system, 5G communication system, such as New Radio (NR) system, and future evolution communication systems, such as 6th generation (6G) mobile communication system, etc.

[0100] In the embodiments of this application, "sending information to...(user equipment or module)" and "sending information to...(user equipment or module)" can be understood as the destination of the information being the user equipment (terminal) or module. This can include sending information directly or indirectly to the user equipment. "Receiving information from...(user equipment or module)" and "receiving information from...(user equipment or module)" can be understood as the source of the information being the user equipment, and can include receiving information directly or indirectly from the user equipment. Information may undergo necessary processing between the source and destination, such as format changes, but the destination can understand the valid information from the source. Similar expressions in this application can be understood in a similar way, and will not be elaborated further here.

[0101] The application scenarios of the embodiments of this application will be described below first.

[0102] Figure 1 This is a schematic diagram of the architecture of the communication system used in the embodiments of this application. Figure 1 As shown, the communication system includes network devices (such as...) Figure 1 110a and 110b, collectively referred to as 110, may also include at least one terminal (such as...). Figure 1 In this application embodiment, 120a-120j are collectively referred to as 120). Figure 1 In the communication system shown, network device 110a has a module capable of implementing radio access network (RAN) functions, and network device 110b can be combined with network device 110a to achieve access to a wireless network, the Internet, or a core network. Network device 100 may also include other devices, such as wireless relay devices and / or wireless backhaul devices. Figure 1 (Not shown in the diagram), wireless relay devices and / or wireless backhaul devices can also be integrated into network device 110. Terminal 120 is connected to network device 110 wirelessly, and network devices 110a and 110b can be connected wirelessly. Different terminals can be interconnected via wired or wireless means.

[0103] In one specific embodiment of this application, network device 110a is a network device that moves relative to the Earth's surface, and network device 110b is a network device that is stationary relative to the Earth's surface.

[0104] At least one of the network devices 110 can also connect to or transmit and receive information with evolved universal terrestrial radio access (E-UTRA), new radio (NR), and future radio access systems or WiFi systems as defined in the 3rd Generation Partnership Project (3GPP). Network device 110 can also connect to devices from two or more of the aforementioned different radio access systems. Network device 110 can also connect to an open radio access network (O-RAN).

[0105] Network device 110 can be used to help terminals access the communication system wirelessly.

[0106] Network device 110a may be configured with a module for implementing base station functions. This module can perform the functions of: a base station, an evolved NodeB (eNodeB or eNB), a transmission reception point (TRP), a next-generation NodeB (gNB) in a 5th generation (5G) mobile communication system, a next-generation base station in a 6th generation (6G) mobile communication system, a base station in a future mobile communication system, or an access node in a WiFi system. The aforementioned base station may include a macro base station, a micro base station, or an indoor station, and may also be a relay node or a donor node. Network device b can cooperate with network device a or independently connect user equipment to the wireless network.

[0107] In another application scenario, multiple wireless access modules can work together to help a terminal achieve wireless access. Different wireless access modules can each implement some functions of the network device 110. For example, a wireless access module can be a central unit (CU), a distributed unit (DU), or a radio unit (RU). The CU can perform the functions of the base station's radio resource control protocol and packet data convergence protocol (PDCP), as well as the service data adaptation protocol (SDAP). The DU performs the functions of the base station's radio link control layer and medium access control (MAC) layer, and can also perform some or all of the physical layer functions. For specific descriptions of these protocol layers, refer to the relevant 3GPP technical specifications. The RU can perform radio frequency signal transmission and reception functions. The CU and DU can be implemented using two independent wireless access modules, or they can be integrated into the same RAN node, such as within a baseband unit (BBU). The RU can be located in radio frequency equipment, such as in a remote radio unit (RRU) or an active antenna unit (AAU). The CU can be further divided into two types: CU-control plane and CU-user plane.

[0108] Terminal 120 can be a device with wireless transceiver capabilities, capable of sending signals to network device 110a, network device 110b, or other devices with signal transceiver capabilities, or receiving signals from network device 110a or network device 110b. In this embodiment, terminal 120 can also be referred to as user equipment (UE), mobile station, mobile terminal, etc. Terminal 120 can be widely used in various scenarios, such as near field communications (NFC) device-to-device (D2D), vehicle-to-everything (V2X) communication, machine-type communication (MTC), Internet of Things (IoT), virtual reality, augmented reality, industrial control, autonomous driving, telemedicine, smart grid, smart furniture, smart office, smart wearables, smart transportation, smart cities, etc. Terminal 120 can be a mobile phone, tablet computer, computer with wireless transceiver function, wearable device, vehicle, airplane, ship, robot, robotic arm, smart home device, air network equipment, ground node, high-altitude base station, etc. The embodiments of this application do not limit the specific technology or device form used in the terminal.

[0109] Communication between network devices and terminals, between network devices 110a and 110b, between terminals and network devices, and between terminals can be conducted using licensed spectrum, unlicensed spectrum, or both simultaneously. Communication can be conducted using spectrum below 6 GHz, spectrum above 6 GHz, or both simultaneously. The embodiments of this application do not limit the spectrum resources used for wireless communication.

[0110] The 5G core network (5G core / new generation core, 5GC / NGC) includes multiple functional units such as access and mobility management function (AMF) network elements, session management function (SMF) network elements, user plane function (UPF) network elements, session function (SF) network elements, authentication server function (AUSF) network elements, policy control function (PCF) network elements, application function (AF) network elements, unified data management (UDM) network elements, and network slice selection function (NSSF) network elements.

[0111] The sensing function (SF) is primarily responsible for sensing control and sensing computation. For example, it can select sensing devices and methods, control sensing services, and process sensing measurement data; it can process sensing measurement data independently. It can also work with the network data analytics function (NWDAF) to achieve intelligent analysis and prediction. The network repository function (NRF) stores the context information of the sensing function, allowing other network elements to discover and select suitable sensing functions through queries.

[0112] Sensing signals are signals used for sensing measurements. Sensing nodes receive sensing signals and perform sensing functions through these measurements. The sensing signals mentioned in this paper include, but are not limited to: positioning reference signal (PRS), sounding reference signal (SRS), channel state information reference signal (CSI-RS), demodulation reference signal (DMRS), phase tracking reference signal (PT-RS), primary synchronization signal (PSS), secondary synchronization signal (SSS), correctly demodulated communication data signals, or dedicated sensing signals, etc., without limitation.

[0113] In the embodiments of this application, the base station function implemented by the network device 110a can also be executed by a module (such as a chip) in the base station, or by a control subsystem containing base station functions. This control subsystem containing base station functions can be a control center in the aforementioned application scenarios such as smart grids, industrial control, intelligent transportation, and smart cities. The terminal function can also be executed by a module (such as a chip or modem) in the terminal, or by a device containing terminal functions.

[0114] In the embodiments of this application, network device 110a can send downlink signals or downlink information to terminal 120 or network device 110b, with the downlink information carried on the downlink channel; terminal 120 can send uplink signals or uplink information to network device 110a or network device 110b, with the uplink information carried on the uplink channel. To communicate with network device 110a, terminal 120 needs to establish a wireless connection in the cell covered by the signal of network device 110a. The cell with which terminal 120 has established a wireless connection can be called the serving cell of the terminal. When terminal 120 communicates with the serving cell, it will also receive signals from neighboring cells.

[0115] In wireless communication systems, signal propagation between network devices and terminals is affected by surrounding objects or the environment. Objects between network devices and terminals may cause changes in the amplitude, phase, and other characteristics of the signal. Therefore, by analyzing the received signal, not only can the communication information carried by the signal be obtained, but also information reflecting the characteristics of the object or environment can be extracted.

[0116] Sensing can refer to the detection of parameters in the physical environment, such as measuring the speed of objects and locating targets. In wireless communication systems, sensing can be achieved by analyzing wireless signals. Furthermore, with the continuous advancement of communication technology, sensing can also become a capability within wireless communication systems, integrating with them to achieve unified communication and sensing. Through sensing capabilities, terminals or network devices can analyze received signals to obtain not only the communication information carried by the signals but also information reflecting the characteristics of objects or the environment.

[0117] Sensing measurement can refer to the process of obtaining information about a perceived target object, including any information associated with the target object such as its position, velocity, angle, time delay, Doppler shift, presence, trajectory, etc. The process of obtaining the perceived target object during sensing measurement can be called sensing, measurement, observation, or determination. Sensing prediction can refer to obtaining measurement values ​​for the next K times based on sensing measurement values ​​from P historical moments. P and K are positive integers. For example, the measured quantity can include position; predicting the position for the next K times based on position measurement values ​​from P historical moments. Similarly, the measured quantity can be signal quality; predicting the signal quality for the next K times based on perceived signal quality measurement values ​​from P historical moments. Furthermore, the measured quantity can be the reference signal transmission-transmitting time difference; obtaining the perceived signal transmission-transmitting time difference for the next K times based on the perceived signal transmission-transmitting time difference (RX-TX time difference) from P historical moments. The prediction method is similar when measuring other quantities. The method provided in this application can be applied to any one or more measured quantities. A set of predicted measurements is defined as the perceived values ​​at P time points and the predicted values ​​at K future time points.

[0118] In the embodiments of this application, the timing of perception prediction can be referred to as the prediction timing. The prediction timing can refer to the measurement value at a corresponding time position obtained through prediction rather than actual measurement, where the corresponding time position is the prediction timing. The prediction timing may differ from the prediction execution time (also referred to as the prediction execution time or prediction occurrence time). The prediction execution time or prediction occurrence time can refer to the time when the prediction is executed, and the execution time when the measurement values ​​for the next K timings are predicted. The time position can be a specific moment or a specific time period.

[0119] In the embodiments of this application, the measurement timing can be considered as the starting point of the sensing measurement or the time period of the sensing measurement.

[0120] Measurement timing and prediction timing can overlap. Measurement timing can be equal to prediction timing, measurement timing and prediction timing can overlap, and the time interval between prediction timing and measurement timing can be small (meeting predefined conditions of the protocol or conditions of the signaling configuration).

[0121] When the measurement timing and the prediction timing overlap, they can be considered related. The prediction timing associated with the measurement timing can be indicated through protocol preset conditions or base station display.

[0122] Predefined conditions can be that the time interval between the prediction timing and the measurement timing is less than or equal to a threshold value. The measurement timing and the prediction timing, which have the closest time intervals or the greatest time overlap, can be considered as correlated if they satisfy the timing correlation condition.

[0123] Figure 2 This demonstrates an implementation architecture for integrated communication and sensing applications in intelligent transportation scenarios. For example... Figure 2 As shown, in the integrated communication and sensing architecture, the network device is base station 21, and the terminal is mobile phone 22. Mobile phone 22 is the sensing signal receiving node in the integrated communication and sensing system. The location of vehicle 23 is the target object to be sensed. Through base station 21 or dedicated vehicle-mounted equipment installed on vehicle 23, the integrated communication and sensing technology can achieve precise positioning, speed monitoring, and traffic flow prediction of vehicle 23, thereby optimizing traffic management and reducing the traffic accident rate.

[0124] Base station 21 can be equipped with a positioning module and a sensing module. By sharing spectrum resources, it can achieve coordinated operation of wireless communication and sensing functions. During the sensing process of mobile phone 22, base station 21 can transmit wireless signals for sensing (which can be called sensing signals). The wireless signals for sensing can be continuous waveforms or specific pulse sequences. The transmitted wireless signals will be reflected when they encounter target objects such as vehicle 23. The integrated communication and sensing architecture can receive the signals reflected by the target objects (which can be called sensing signals) through mobile phone 22 and perform preliminary filtering and amplification processing. Mobile phone 22 can further process and analyze the received reflected signals, including signal demodulation, filtering, and feature extraction. Mobile phone 22 analyzes the processed signals to extract information such as the position, speed, and direction of vehicle 23. Based on the signal processing results, mobile phone 22 and base station 21 can determine the specific location of the vehicle. Through continuous signal transmission and reception, mobile phone 22, base station 21, and vehicle 23 can also achieve real-time tracking and updating of the vehicle 23's position. By accurately locating vehicle 23, monitoring its speed, and predicting traffic flow, traffic management can be optimized and the traffic accident rate can be reduced.

[0125] As wireless communication and sensing increasingly overlap in their operating frequency bands, both technologies are converging towards large-scale antenna arrays. This allows them to share some hardware, and future wireless communication systems are expected to possess both communication and sensing capabilities simultaneously. With the development of new services such as digital twins and vehicle-to-everything (V2X) communication, wireless communication systems and radar sensing systems may converge in terms of spectrum, technological trends, and applications. Currently, integrated communication and sensing technology is primarily in a stage where wireless communication and sensing coexist. The performance of wireless communication and sensing in the system is mutually constrained, and the resources used by sensing are originally used by wireless communication. Therefore, sensing may reduce the available resources for wireless communication, necessitating research into methods to improve sensing accuracy and reduce sensing overhead.

[0126] Therefore, one embodiment of this application introduces a prediction function into an integrated communication and sensing architecture. This prediction function enables the prediction of the location of the sensed target object. Based on the prediction result, sensing resources (such as adjusting the time interval and power of sensing measurements) can be adaptively adjusted. Figure 3A , Figure 3B In the scenario shown, the position of the aircraft can be the perceived target object, and the aircraft trajectory can be predicted. Figure 3A , Figure 3B The solid line in the figure represents the measured value, indicating the actual position of the aircraft. Figure 3A , Figure 3B The dashed line in the diagram represents the predicted value, indicating the position obtained by predicting the aircraft's location. The predicted position may differ significantly from the actual position, such as... Figure 3B Alternatively, the predicted location may differ slightly from the actual location, such as... Figure 3A Thus in Figure 3B In the scenario shown, the prediction frequency can be reduced while maintaining the proportion of wireless communication resources occupied by sensing. Figure 3A As shown, the frequency and time interval of sensing can be adjusted to reduce the proportion of wireless communication resources occupied by sensing and improve the utilization rate of the predictive function in the integrated communication and sensing architecture.

[0127] exist Figure 3A , Figure 3B In the scenario shown, the prediction function can be implemented by a mobile phone or a base station. After integrating the prediction function into the integrated communication and sensing architecture, the node implementing the prediction function needs to be pre-configured with the corresponding other nodes for the prediction function. Therefore, this application embodiment provides a sensing prediction method, such as... Figure 4 As shown, it includes steps S41 to S44.

[0128] Step S41: The terminal sends its prediction capability information to the network device.

[0129] Correspondingly, the network device receives information about the terminal's prediction capabilities. This prediction capability information refers to the terminal's ability to predict the location of a perceived target object. The terminal's prediction capability information can include multiple aspects. For example, on one hand, it can indicate whether the terminal possesses prediction capabilities. On the other hand, it can indicate the accuracy of the prediction information provided by the terminal. In one possible implementation, the terminal's prediction capability information can include at least one of the following: indication information on whether the terminal can predict the location of the perceived target object, and the terminal's historical prediction error. The terminal's historical prediction error can refer to the error between the historical prediction information obtained by the terminal in predicting the perceived target object and the actual result of the perceived target object.

[0130] In possible implementations, the terminal's predictive capability information may differ depending on the environment. Alternatively, the terminal's predictive capability information may also differ depending on the perceived target object. The perceived target object can refer to a single object or environment that requires information acquisition, detection, extraction, and identification through the communication system.

[0131] The aforementioned environment can be an organic whole composed of multiple target objects. The perceived target object may include a single object or a combination of multiple different objects. For example, the perceived target object may include at least one of the following types of objects.

[0132] Target Object 1: Parameters in the physical environment. Parameters in the physical environment include physical quantities such as temperature, humidity, pressure, displacement, and velocity. These parameters are typically sensed by appropriate sensors and converted into electrical signals or other processable signal forms.

[0133] Target Object Two: Electromagnetic Waves. In wireless communication, the sensed target object can be the electromagnetic wave itself. Electromagnetic waves can include, for example, radio waves and microwaves. When electromagnetic waves are sensed as a target object, they can carry special information, which can be received and identified by the integrated communication and sensing architecture through devices such as antennas.

[0134] Target Object 3: Target Objects. Target objects can include moving or stationary objects such as animals, vehicles, and people. These objects can be detected and identified using sensing devices such as radar and cameras within the integrated communication and sensing architecture.

[0135] Target object four: Chemical environment. The chemical environment may include, for example, the concentration of harmful gases in the air and the content of pollutants in water. The chemical environment can be sensed through a communication and sensing integrated architecture using specialized chemical sensors.

[0136] The predictive capabilities of different terminals may vary due to factors such as hardware and software configurations. In possible implementations, the terminal's predictive capability information may include: information indirectly indicating the terminal's ability to predict target objects, and / or information directly indicating the terminal's ability to predict target objects.

[0137] The information indirectly indicating the terminal's ability to predict the target object may include at least one of the following: the terminal's hardware conditions, data computation software capabilities, data analysis software capabilities, remaining battery power, and instruction execution capabilities. The information directly indicating the terminal's ability to predict the target object may include: indicators representing the terminal's ability to predict the target object (high or low), prediction time, and prediction error. For example, A might represent high capability, B medium capability, and C low capability. Alternatively, an error rate could be used as a percentage to represent the terminal's ability to predict the target object (high or low).

[0138] In one possible implementation, the terminal's predictive capability information further includes information on how to invoke the terminal's predictive capability. This information may include at least one of the following: how to enable or disable the terminal's predictive capability, whether the activation of the terminal's predictive capability is triggered based on conditions or events, and the conditions for triggering the activation of the terminal's predictive capability. If the terminal's predictive capability information includes the conditions for triggering the activation of the terminal's predictive capability, it indirectly indicates that the terminal's predictive capability can be triggered based on conditions or events.

[0139] Step S42: The terminal receives measurement configuration indication information sent by the network device; the measurement configuration includes: prediction and measurement configuration.

[0140] Accordingly, in step S42, the network device sends measurement configuration indication information to the terminal. The measurement configuration indication information can be used to indicate the measurement configuration. When executing step S42, the terminal enters a state of radio resource control (RRC) connection with the network device (also referred to as the terminal being in RRC connection state). The measurement configuration indication information can be used to indicate different sensing methods when the terminal is in RRC connection state, and / or the sensing information of the measurement configuration can be associated with different cells.

[0141] The above-described configuration of prediction and measurement can be used to indicate the temporal location information of the prediction and / or measurement. The temporal location information of the prediction and / or measurement may include at least one of the following: the occurrence time of the prediction and / or measurement, the prediction timing information within the prediction measurement group, and the temporal pattern configuration of the alternation of prediction and / or measurement.

[0142] In this embodiment of the application, the measurement can be based on prediction, which means that after at least one prediction is completed, the measurement is performed based on the results obtained from the at least one prediction. Therefore, the configuration of prediction and measurement can include a correspondence between at least one prediction and at least one measurement.

[0143] In one possible implementation, the configuration for prediction and measurement may include configuration information for measurement and configuration information for prediction. The configuration information for measurement may include at least one of the following: the target object of the measurement, the quantity being measured, the method of execution of the measurement, and the time of measurement. The configuration information for prediction may include at least one of the following: the target object of the prediction, the type of prediction, the method of execution of the prediction, and the time of prediction.

[0144] The aforementioned measurement execution method can refer to the sensing measurement method, such as using 3GPP sensing or non-3GPP sensing to measure resources, including time-frequency location and spatial resources, and the transmit power of sensing signals.

[0145] Indicative information for predicted measurements, used to indicate which measurements to predict. Methods for performing the prediction: including the prediction method itself, such as Kalman filter-based methods or prediction methods based on artificial intelligence (AI) / machine learning (ML).

[0146] In one possible implementation, the configuration for prediction and measurement can be determined based on prediction capability information reported by the terminal to the network device. On the network device side, the configuration for prediction and measurement sent to a specific terminal can be determined at least based on the prediction capability information. The network device can also determine the configuration for prediction and measurement sent to the terminal based on the type of network device, the type of terminal, and the target object being sensed.

[0147] Step S43: The terminal sends measurement information to the network device based at least in part on the indication information.

[0148] Measurement information refers to the information obtained by measuring a perceived target object. Measurement information may include: the measured value of the quantity at the current moment, the measured values ​​of the quantity at P historical moments, the measurement uncertainty, the quality of the sensed signal, and other possible additional information.

[0149] Sending measurement information to a network device, at least partially based on indication information, can mean that when a terminal sends measurement information to a network device, it can obtain the measurement information based on the indication information and other information and then send it to the network device. Simultaneously, sending measurement information to a network device, at least partially based on indication information, can include: receiving a sensing signal, at least partially based on the indication information, and sending the measurement information carrying the prediction information to the first device based on the received sensing signal.

[0150] In this embodiment, the terminal can send measurement information to the network device according to the configuration of the prediction and measurement methods in the instruction information. The measurement information can be sent after the corresponding prediction information is sent. The measurement information can refer to the measurement result. Alternatively, the terminal's measurement information can refer to the result obtained by the terminal measuring the target object being measured.

[0151] The content included in the measurement information can be configured through indication information. For at least one prediction, the terminal sends at least one measurement message to the network device. A prediction message can include predicted values ​​for K future time points, while a measurement message can be information generated from a single measurement.

[0152] The indication information may also include the parameters to be measured, the measurement period, the method of transmitting the measurement information, and the format of the measurement information. Based on the parameters in the indication information, the user equipment performs a prediction within a set of prediction and measurement occurrence periods, after the measurement has occurred.

[0153] Measurement information can be sent to network devices in the form of measurement reports. During the measurement process, the terminal processes the measured data and generates a measurement report. The measurement report may contain measurement information for multiple candidate cells, such as the first measurement information corresponding to the first candidate cell and the second measurement information corresponding to each second candidate cell.

[0154] Step S44: The terminal sends prediction information to the network device based at least in part on the indication information.

[0155] Steps S43 and S44 can be executed in any order or simultaneously. Prediction information refers to information obtained by predicting the perceived target object. Prediction information may include predicted values ​​for the next K time points (K is a positive integer), prediction timestamps, prediction time intervals, expected prediction time location information, and measurement time location information. Measurements of prediction error over a certain period, such as the maximum prediction error corresponding to different prediction durations, are used to activate or deactivate the prediction function, adjust the measurement cycle, prediction cycle, and other prediction-related additional information.

[0156] Sending prediction information to network devices, at least in part based on indication information, can mean that when a terminal sends prediction information to a network device, it can obtain prediction information based on indication information and other information and send it to the network device.

[0157] In step S44, the terminal sends prediction information to the network device according to the configuration of the prediction and measurement methods in the instruction information. In this embodiment, the terminal may first perform at least one measurement on the object being measured, and then, at or after the generation of the at least one measurement information, perform at least one prediction on the target being measured to obtain prediction information. Thus, the at least one measurement and the subsequent at least one prediction can constitute a set of predictions and measurements.

[0158] In this embodiment of the application, the prediction information can refer to the prediction result. Alternatively, the terminal's prediction information can refer to the result obtained by the terminal in predicting the target object being measured.

[0159] The content included in the prediction information may be the same as that included in the measurement information. Alternatively, the content included in the prediction information may be partially the same as that included in the measurement information. Or, the prediction information may include at least a portion of the content from the measurement information. For example, the measurement information may include both image data and location data, while the prediction information may only include location data.

[0160] In step S44, the terminal needs to predict the target object being measured and generate prediction information. The terminal can generate prediction information in at least one of the following ways.

[0161] One approach is to deploy a predictive model on the terminal to generate forecast information based on historical data corresponding to the target object being measured. Alternatively, the terminal can also call an online predictive model to process historical data and obtain forecast information.

[0162] Alternatively, the terminal can use a pre-defined prediction algorithm to calculate and generate prediction information based on historical data corresponding to the target object being measured. Or, the terminal can use an external device to calculate and generate prediction information from historical data.

[0163] Thirdly, the terminal can calculate and generate prediction information based on historical data or other variables related to the target object. Alternatively, the terminal can generate prediction information based on the properties of the target object and the prediction algorithm. Or, the terminal can also generate prediction information based on the properties of the target object and the prediction model.

[0164] The method provided in this application embodiment enables the terminal to process prediction and measurement according to certain rules based on the indication information of the measurement configuration, and to reasonably play the predictive function in the measurement process.

[0165] In possible implementations, the configuration of the prediction and measurement may include multiple pieces of information. For example, one of the pieces of information that may be included in the configuration of the prediction and measurement is at least one of a first time interval, a second time interval, a third time interval, and a fourth time interval.

[0166] The first time interval can also be called the first cycle, the second time interval can also be called the second cycle, the third time interval can also be called the third cycle, and the fourth time interval can also be called the fourth cycle. When the terminal performs prediction and measurement, the prediction and measurement are performed regularly according to multiple groups. Within each group, the terminal repeatedly executes one prediction and measurement at the specified time position. Within a group of predictions and measurements, the predictions and measurements are executed at the same time intervals. Therefore, the configuration of prediction and measurement can include multiple time intervals, each reflecting the time position information of the measurement and prediction.

[0167] In a possible implementation, the first time interval is used to indicate the time interval of a prediction within a set of predictions and measurements. Alternatively, the first time interval can be used to indicate the time interval of a prediction within a set of at least one consecutive prediction. Alternatively, the first time interval can also be referred to as the prediction period.

[0168] In a possible implementation, the second time interval is used to indicate the time interval between the x-th prediction in a set of at least one consecutive predictions and the x-th prediction in the next set of at least one consecutive predictions, where x is less than or equal to K, and K is the number of times the first time interval occurs within the second time interval. For example, the second time interval can be the time interval between the first prediction in a set of at least one consecutive predictions and the first prediction in the next set of at least one consecutive predictions. Alternatively, the second time interval can be the time interval between predictions corresponding to different sets. Therefore, the second time interval can also be considered as a repeating time interval of a set of predictions and measurements, or the second time interval can also be referred to as the period of a repeating pattern of predictions and measurements.

[0169] In one possible implementation, the configuration for prediction and measurement may further include a third time interval and a fourth time interval. The third time interval can be used to indicate the repetition time interval of the measurement within the second time interval. The repetition time interval of the measurement within the second time interval can also be referred to as the repetition period of the measurement within the second time interval. The third time interval can be equal to the first time interval, for example, in... Figure 6 In the example shown, each box annotated "Prediction Timing" represents a prediction, and each box annotated "Measurement Timing" represents a measurement. The second time interval is equal to four first time intervals or four third time intervals. The third time interval may also not be equal to the first time interval, such as... Figure 7In the example shown, the first time interval can be greater than the third time interval.

[0170] In a possible implementation, the third time interval is used to indicate the time interval between two adjacent measurements within the second time interval. The second time interval can be the time interval between a set of predictions and measurements and the next set of predictions and measurements, and the third time interval can be considered as the period of measurement. Within the same second time interval, the time intervals of measurements can be considered the same.

[0171] In a possible implementation, the fourth time interval is used to indicate the time interval between the y-th measurement in a set of at least one consecutive measurements and the y-th measurement in the next adjacent set of at least one consecutive measurements, where y is less than or equal to P, and P is the number of times the third time interval occurs within the fourth time interval. If the previous set of measurements is performed during prediction, the third time interval may be equal to the first time interval in the temporal sequence of the first set of predictions and measurements. If the previous set of measurements is not performed during prediction, the third time interval may be equal to the second time interval.

[0172] In possible implementations, the configuration for prediction and measurement may include two pieces of information: the number of times the first time interval occurs within the second time interval, K, and the number of times the third time interval occurs within the second time interval, P. Wherein, P or K can default to 1 if not explicitly configured, or be processed according to the default values ​​specified in the protocol.

[0173] The number of times the first time interval occurs within the second time interval can be considered as the number of prediction opportunities within the second time interval. In the embodiments of this application, prediction is performed after measurement in each set of predictions and measurements. In a set of predictions and measurements, there may be one prediction or multiple predictions. Next, the situation corresponding to one or more measurement opportunities in a set of predictions and measurements will be described.

[0174] In one implementation, multiple predictions can be made based on a single measurement within a set of predictions and measurements, i.e., P=1. Configuration parameters include: a prediction time interval Δt1 (equivalent to the first time interval in the aforementioned embodiment), and a set of prediction and measurement time intervals Δt2 (equivalent to the second time interval in the aforementioned embodiment). Figure 5 As shown, each box annotated "Prediction Timing" represents the I-th prediction timing (I≤K) in a prediction. Each box annotated "Measurement Timing" represents a measurement. In the embodiments of this application, timing can also be referred to as moment or time window. Figure 5 In the example shown, the second time interval equals the fourth time interval. Assuming the number of predictions in a set of predictions and measurements is K, taking K=4 as an example, for overlapping times, i.e. Figure 5The scenario shown, where prediction timing 4 and measurement timing 2 overlap (or, where prediction timing 4 and measurement timing 2 occur simultaneously), involves both prediction and measurement. For example, the association between prediction and measurement timings can be determined using the time and location information of prediction and measurement, as well as predefined association conditions (such as prediction and measurement timings being less than or equal to a threshold). It can be assumed that the measurement and prediction timings with the closest time intervals or the greatest time overlap that satisfy the timing association conditions are associated. Alternatively, the base station can display an indication of the prediction timing associated with the measurement timing.

[0175] like Figure 5 As shown, prediction timing 4 is associated with measurement timing 2, and the prediction error is obtained at least in part based on the associated prediction timing 4 and measurement timing 2. Prediction error can be obtained at the time when prediction timing 4 and measurement timing 2 overlap. The timing of at least one measurement value in group X+1 and at least one prediction value in group X overlaps, and the prediction error is calculated at least in part based on the measurement value of the associated measurement timing and the prediction value of the prediction timing, obtained by the terminal or network device based on prediction information and measurement information. Alternatively, in a prediction measurement group, the prediction error is obtained through at least one measurement and at least one prediction value from the previous prediction measurement group, obtained by the terminal or network device based on prediction information and measurement information. After measurement timing 2 completes the measurement and the prediction for the next four timings is completed, the first information is reported, which includes at least one or a combination of measurement information, prediction information, and prediction error.

[0176] Next, we will introduce how to calculate the prediction error. Prediction error can be used to evaluate the accuracy of predictions. Let... For the predicted value, y i For each observation, n is the number of observations. Measured values ​​can be labels and / or true values.

[0177] The prediction error at a certain time can be used The maximum prediction error over a given time period can be measured using at least one of the following measures: the maximum prediction error, the root mean square error (RMSE), the average prediction error, and the variance of the prediction error. The calculation methods are as follows.

[0178] The formula for calculating the root mean square error of the prediction error is:

[0179]

[0180] The formula for calculating the average prediction error is:

[0181]

[0182] The formula for calculating the variance of the prediction error is:

[0183]

[0184] Optionally, the prediction and measurement configuration may also include a set of prediction opportunities within the measurement and prediction time intervals. The prediction opportunity number represents the number of prediction opportunities, which can be determined by the two time intervals Δt1 and Δt2 mentioned above, or can be set directly in the prediction and measurement configuration.

[0185] In another implementation: K predictions can be made based on P measurements from a set of predictions and measurements, where P and K are both integers greater than 1. For example... Figure 6 As shown, P can be 2 and K can be 4. This means that for every two consecutive measurements, the measurement values ​​for four future time points are predicted. Within a set of predictions and measurements, there may be overlap between measurement and prediction time points. In the second or subsequent sets of predictions and measurements implemented at the terminal, the remaining two prediction time points from the previous adjacent set may overlap with the measurement time points of the current set. (Refer to...) Figure 6 As shown, measurement timing 3 and measurement timing 4 overlap with prediction timing 3 and prediction timing 4, respectively. Each set of measurements and predictions includes 2 measurement timings and 4 prediction timings. In each set of measurements and predictions after the first set, the last two predicted values ​​in each set overlap with the time positions of the two measurement values ​​in the next adjacent set.

[0186] In one possible implementation, the configuration for prediction and measurement may further include: the number P of the third time interval within the fourth time interval. Alternatively, if the second time interval is equal to the fourth time interval, P is the number of the third time interval within the second time interval. The number P of the third time interval can be used to calculate the number of measurements within the second time interval. For example, in... Figure 7 In the example shown, P is 2.

[0187] Optionally, the configuration for prediction and measurement may also include the number of prediction opportunities K within the second time interval. The number of prediction opportunities within the second time interval can also be implicitly determined based on the first and second time intervals. Generally, the number of predictions within the second time interval is greater than or equal to the number of measurements. Simultaneously, the number of predictions multiplied by a factor K1 may equal the second time interval, thus: Δt2 = K1 × Δt1. Therefore, based on the relationship between the first and second time intervals, the number of predictions = factor = K1. When the number of measurements is represented as P, the number of predictions within a second time interval can include K1-P predictions within the current second time interval, and P predictions within the next adjacent second time interval.

[0188] In a measurement and prediction group, when making predictions for multiple measurements, the terminal or base station can support predictions based on the most recent P historical measurements. For example... Figure 6 As shown, taking P=2 and K=4 as an example, the number of predictions is 4 in the second time interval. The terminal or base station can make predictions based on the two most recent measurements in history.

[0189] In possible implementations, in addition to the prediction and measurement configuration, the measurement configuration may also include other information. For example, the measurement configuration may include: the predicted measurement quantity and / or prediction configuration information. The prediction configuration information may include at least one of the following: prediction and measurement configuration, indication information for enabling / disabling prediction, indication information for sending measurement reports carrying prediction information, prediction auxiliary information, a detection time window for prediction errors, and measurement offset values.

[0190] The measurement report transmission instruction information carrying the prediction information can be represented as: T offset The instruction information for sending measurement reports carrying forecast information can be determined using at least two of the following methods.

[0191] Method 1: First T offset The reporting time offset value relative to measurement reports that do not carry forecast information. Method 2: Second T offset The time interval from the last symbol of the sensed signal to the transmission of the measurement report carrying the prediction information within this time interval.

[0192] The auxiliary information for the above prediction may include: M historical measurements no earlier than the first timing point and / or the target motion model. Historical measurements may include information such as the position and / or velocity of the target at historical timing points. The target motion model may be at least one of the following: a uniform motion model, a uniformly accelerated motion model, a nonlinear model, etc.

[0193] The determination method for the detection time window and measurement offset value of the above prediction error can be referred to Figure 10As shown, taking a measurement value including a delay measurement value as an example, the delay measurement value represents the time it takes for the sensing signal to travel from the sensing signal transmitting device to the sensing signal receiving device. Within the prediction error detection time window, there are 4 delay measurements, each corresponding to a predicted value for one prediction opportunity. If the size of the prediction and measurement group is 4, the delay value for the next 4 opportunities is predicted at once based on historical observations. If the size of the prediction group is 2, then the number of prediction groups is 2, and the delay value for the next 2 opportunities is predicted at once based on historical observations. The measurement offset value indicates how many measurement values ​​are offset in each prediction. If the measurement offset value is 2, then 2 measurement values ​​are offset each time for K predictions. The prediction and measurement group is different from the prediction measurement group in the previous embodiment; the prediction and measurement group can include both a prediction group and a measurement group. The size of the prediction group can be used to represent the number of prediction opportunities for performing one prediction. For example, if the size of the prediction group is 4, then one prediction will predict the measurement value for the next 4 opportunities. The number of predicted groups refers to the number of predicted groups contained within the prediction error detection time window. Its value is equal to the ratio of the total number of prediction opportunities / measurements within the prediction error detection time window to the size of the predicted groups. If the ratio is not an integer, it can be rounded down. For example, if the predicted group size is 3, the number of predicted groups is floor(4 / 3) = 1. floor() is the floor function.

[0194] In the embodiments of this application, in Figure 4 Based on the method shown, in steps S43 and S44, the measurement information and prediction information can be sent from the terminal to the network device as part of the first information. Simultaneously, the first information may also include other possible auxiliary information. For example, the first information may include at least one of the following: the time interval for the next K time points, the timestamp of the prediction time point, or the prediction time interval, the expected configuration of prediction and measurement, a measure of prediction error over a certain period, the measurement value at the current time point, the prediction values ​​for the next K time points, the prediction error at the current time point, and whether the reported measurement value is based on the prediction information. Measurements based on prediction may have higher confidence and can be processed preferentially by the second device.

[0195] The desired configuration for prediction and measurement might include: the maximum prediction duration to meet sensing requirements and the number of measurements required. Measuring the prediction error over a given time period can include the maximum prediction error corresponding to different prediction durations, and this measurement can be used to activate or deactivate the prediction function, adjust the measurement interval, and the prediction interval. The measurement value at the current moment and the prediction values ​​for the next K measurements can be used to adjust the beam pointing, power, etc., in advance for the next moment. The prediction error at the current moment... Used to determine the reliability of prediction results / / reliability of measurement results. Reported measurement information may include: distance, speed, etc. If the measurement information includes the target object being measured, the reported measurement information can be the measured value.

[0196] Next, in Figure 4 Based on the illustrated embodiment, another implementation of step S41 can be as follows: Figure 8 Steps S81 and S82 are shown.

[0197] Step S81: The terminal receives a capability information request message from the network device for a node B (g node B, gNB) or a sensing function (SF) in the core network.

[0198] The aforementioned capability information request message is used to request the terminal to send its capability information to the network device. The capability information request message may include specific capability information specifications, which can be used to request the terminal to report whether it possesses the specified capability information.

[0199] Step S82: The terminal sends the specified capability information to the gNB / SF according to the capability information request message.

[0200] In possible implementations, the specified capability information may include predicted capability information and / or measured capability information. For example, the predicted capability information may include at least one of the following: whether perceptual prediction is supported, the supported prediction types, and the prediction processing time.

[0201] The aforementioned prediction processing time can be used to determine the time required to send a measurement report carrying prediction information. In other words, sufficient time is reserved for prediction execution between sending the measurement report. Specifically, the prediction processing time can be the processing time for predicting measurement information for K future time points based on P historical results. The preprocessing time can be determined in at least two ways: Method 1: The preprocessing time can be the maximum prediction processing time. Method 2: The preprocessing time can be indicated in the form of levels, with different levels or the maximum prediction processing time specified in the protocol. For example... Figure 9 As shown, for timing 1, only measurement information is reported (that is, the measurement report may not include prediction information), and the time from the last symbol of the sensed signal to the transmission of the measurement report must be ≥ T. proc =T measure For timing 2, both measurement and prediction results need to be reported, and the time from receiving the last symbol of the sensing signal to sending the measurement report must be ≥ T′. proc =T measure +T predict .

[0202] In possible implementations, the prediction type mentioned above may include a first type and a second type, which are described below.

[0203] The predictive information obtained from the first type of capability information may include intermediate measurements of the predicted sensing. For example, the predictive information obtained from the first type of capability information may include at least one of the following: sensed signal quality, delay estimate, Doppler estimate, angle estimate, reference signal time difference (RSTD), reference signal time of arrival (RTOA), reference signal time difference (RSTD), reference signal transmit-receive time difference (RX-TX time difference), reference signal carrier phase (RSCP), carrier phase difference (RSCPD), azimuth angle of arrival, azimuth angle of departure, elevation angle of arrival, and elevation angle of departure.

[0204] The aforementioned perceived signal quality refers to the subjective or objective evaluation of the received signal quality by a user or device, encompassing aspects such as signal strength, stability, clarity, and whether it meets specific application requirements. Perceived signal quality is a crucial indicator for measuring system performance, user experience, and device compatibility. High perceived signal quality ensures accurate information transmission and reception, improves communication efficiency, reduces bit error rate, and thus enhances user experience. Measurements characterizing perceived signal quality include, but are not limited to: the signal-to-interference-plus-noise ratio (SINR), signal-to-noise ratio (SNR), reference signal received power (RSRP), reference signal received quality (RSRQ), and the SINR, SNR, RSRP, and RSRQ of the i-th path of the perceived signal.

[0205] The aforementioned signal-to-interference-plus-noise ratio (SNR) can refer to the ratio of the expected received signal power to the sum of the interfering signal power and noise power in a specific communication link. The aforementioned SNR can also refer to the proportional relationship between signal power and noise power. The aforementioned reference signal received power can be used to measure the power of the downlink reference signal received by the user equipment from the serving cell or neighboring cells. The aforementioned reference signal received quality can refer to the ratio of the reference signal received power to the total power within the received signal bandwidth.

[0206] The SINR of the i-th path of the sensed signal can refer to the SINR of the sensed signal on the i-th propagation path. The SNR of the i-th path of the sensed signal can refer to the ratio of the sensed signal power to the noise power on the i-th propagation path. The RSRP of the i-th path of the sensed signal can refer to the RSRP of the sensed signal on the i-th propagation path. The RSRQ of the i-th path of the sensed signal can refer to the RSRQ of the sensed signal on the i-th propagation path.

[0207] The aforementioned delay estimate can be a value obtained by estimating the signal transmission delay. This delay estimate can be used to measure the time required for a signal to travel from the transmitter to the receiver. In possible implementations, the time delay between signals can be calculated using a defined calculation method.

[0208] The aforementioned Doppler estimates can be parameter estimates obtained through Doppler effect measurements. These parameter estimates may include, but are not limited to, at least one of the following: the velocity, angle, and distance of the target object. The Doppler effect refers to the change in the frequency of the wave received by the observer when there is relative motion between the wave source and the observer. According to the Doppler effect, the received frequency increases when the wave source moves closer to the observer; conversely, the received frequency decreases when the wave source moves away from the observer.

[0209] An angle estimate can be a numerical value obtained by estimating the angle of an object or signal relative to a reference direction using measuring equipment or algorithms. Methods for obtaining angle estimates typically include those based on geometric relationships, physical laws, or signal processing algorithms, and involve measuring the angle between the object or signal and the reference direction.

[0210] The reference signal arrival time can refer to the time recorded by the receiver after a known, specific signal (i.e., the reference signal) is sent from the transmitter.

[0211] Reference signal time difference refers to the time difference between reference signals received by a user equipment from two different base stations (or transmit points). Reference signals are signals for which the transmitting and receiving ends know information. Reference signals mentioned in this article include, but are not limited to: positioning reference signal (PRS), sounding reference signal (SRS), sensing reference signal, CSI-RS, DMRS, PT-RS, PSS, SSS, and correctly demodulated communication data signals, etc., without limitation.

[0212] The reference signal transmission and reception time difference includes the user equipment transmission and reception time difference (UE Rx-Tx time difference) and the base station transmission and reception time difference (gNB Rx–Tx time difference).

[0213] UE reception time (TUE-RX) can be the timing of the downlink subframe #i received by the user equipment from the transmission point, defined by the first detected path in time.

[0214] The UE transmission time (TUE-TX) is the transmission time of the user equipment (UE) uplink subframe #j, which is the time closest to the subframe #i received from the transmission point (TP).

[0215] The base station reception time (TgNB-RX) is the time when the transmit and receive point (TRP) receives the uplink subframe #i containing sensing signals (such as SRS) related to the user equipment (UE), defined by the first detected path in time.

[0216] The base station transmit time (TgNB-TX) is the transmission time of downlink subframe #j at the Transmit and Receive Point (TRP), which is the time closest to subframe #i received from the User Equipment (UE).

[0217] UE Rx–Tx time difference = TUE-RX–TUE-TX; where TUE-RX is the UE reception time; TUE-TX is the UE transmission time.

[0218] gNB Rx–Tx time difference=TgNB-RX–TgNB-TX.

[0219] Reference signal carrier phase (RSCP) can be defined as the phase of the channel response derived from the first path delay of the resource element carrying the sensing signal (such as PRS, SRS) used for measurement.

[0220] The definitions of azimuth arrival angle, azimuth departure angle, pitch arrival angle, pitch departure angle, and all the above-mentioned measurement quantities can be found in the TS 38.215 protocol (V18).

[0221] The predictive information obtained from the second type of capability information may include the final result of the predicted perception. For example, the final result of the predicted perception may include at least one of the following: the location of the perceived target object, the trajectory of the perceived target object, and whether the perceived target object exists.

[0222] Prediction processing time refers to the processing time for predicting K future timing observations based on P historical results. Method 1 can be the maximum prediction processing time. Method 2 can be indicated through levels, with different levels corresponding to preset maximum prediction processing times. For example... Figure 5 As shown, for measurement timing 1, only the measured value is reported, and the time from the last symbol of the sensed signal to the sending of the measurement report must be ≥ T. proc =T measure For timing 2, both measurement and prediction results need to be reported, and the time from receiving the last symbol of the sensing signal to sending the measurement report must be ≥ T′. proc =T measure +T predict .

[0223] The implementation methods for steps S83 to S86 can be referred to Figure 4 Steps S42 to S45 in the illustrated embodiment.

[0224] The perception prediction method provided in this application can be used not only for user equipment to implement perception prediction, but also for base stations to implement perception prediction. In another example of this application, taking the interaction between user equipment and the core network to implement perception prediction as an example, such as... Figure 11 As shown, the perception prediction method may include the following steps S111 to S117.

[0225] Step S111: The user equipment reports the measurement prediction capability information to SF.

[0226] Step S112: The base station receives the measurement configuration indication information, which includes the prediction and measurement configuration.

[0227] Step S113: The user equipment receives the measurement configuration indication information sent by the SF. The measurement configuration indication information includes the configuration of prediction and measurement.

[0228] In other possible implementations of step S113, the user equipment may receive measurement configuration indication information sent by the gNB.

[0229] Step S114: The sensing network element requests sensing information from the sensing node.

[0230] Step S115: The sensing network element requests sensing information from the sensing receiving node.

[0231] The sensing transmitting node can be a base station, used to transmit sensing signals. The sensing signals may include a first signal emitted towards the sensed target object for sensing, and a second signal emitted after the first signal passes through the sensed target object. The sensing receiving node can be a user equipment.

[0232] Step S116: The sensing and transmitting node sends a sensing signal.

[0233] Step S117: The user equipment receives the sensing signal and performs sensing measurement.

[0234] In step S117, the user equipment also calculates the predicted values ​​of relevant measurements and / or the prediction error according to the instruction information. When the user equipment interacts with the base station to achieve perception prediction, steps S114 to S117 are implemented in the same way.

[0235] Step S118: The user equipment sends the first information to the SF.

[0236] The first information includes at least predictive information, and may also include measurement information, etc.

[0237] exist Figure 11 In the illustrated embodiment, the dashed line represents the access management function (AMF), indicating that signaling between the base station and the SF network element can be transparently transmitted through the AMF, or it can bypass the AMF and directly interact with the base station between the sensing network elements. In other possible implementations, the user equipment can interact with the base station to achieve sensing and prediction. That is, in other possible implementations, Figure 11 The functions of the SF shown can be achieved through a base station.

[0238] In another example of this application, the interaction between the base station and the core network is used to achieve sensing and prediction, and the prediction is performed by the base station. Figure 12 As shown, the perception prediction method may include the following steps S121 to S127.

[0239] Step S121: The base station reports the measurement prediction capability information to SF.

[0240] Step S122: The base station receives the measurement configuration indication information sent by SF. The measurement configuration indication information includes the prediction and measurement configuration.

[0241] Step S123: SF sends a request for sensing information to the sensing node.

[0242] Step S124: SF requests sensing information from the sensing receiving node.

[0243] In this example, the sensing transmitting node can be a user equipment, and the sensing receiving node can be a base station.

[0244] Step S125: The user equipment sends a sensing signal.

[0245] Step S126: The base station receives the sensing signal and performs sensing measurements.

[0246] Step S127: The base station sends first information to SF. The first information may include measurement information and prediction information.

[0247] In another example of this application, taking the interaction between the base station and the core network to achieve perception and prediction, and the prediction being performed by the core network, as an example, Figure 13 As shown, the perception prediction method may include the following steps S131 to S137.

[0248] Step S131: The base station receives the measurement configuration indication information sent by SF. The measurement configuration indication information includes the prediction and measurement configuration.

[0249] Step S132: SF sends a request for sensing information to the sensing node.

[0250] Step S133: SF requests sensing information from the sensing receiving node.

[0251] In this example, the sensing transmitting node can be a user equipment, and the sensing receiving node can be a base station.

[0252] Step S134: The user equipment sends a sensing signal.

[0253] Step S135: The base station receives the sensing signal and performs sensing measurements.

[0254] Step S136: The base station sends measurement information to SF.

[0255] Step S137: SF makes a prediction based on the measurement information and sends the prediction information to the base station.

[0256] In another example of this application, taking the interaction between a user equipment and a base station to achieve perception prediction as an example, such as... Figure 14 As shown, the perception prediction method may include the following steps S141 to S147.

[0257] Step S141: The user equipment reports the measurement prediction capability information to the base station.

[0258] Step S142: The user equipment receives the measurement configuration indication information sent by the base station.

[0259] In addition to the information in other embodiments of this application, the indication information of the measurement configuration may also include at least one of the following: whether event triggering is enabled, event type, and threshold.

[0260] The above event types can correspond to thresholds. For example: Event Type 1: When the prediction error is less than threshold 1 for A consecutive times, prediction is enabled. Event Type 2: When the prediction error is greater than threshold 2 for K consecutive times, prediction is disabled. Event Type 3: When the prediction error is less than threshold 3 more than B times within a preset time, prediction is enabled. Event Type 4: When the prediction error is greater than threshold 4 more than B times within a preset time, prediction is disabled.

[0261] Step S143: SF sends a request for sensing information to the sensing node.

[0262] Step S144: SF requests sensing information from the sensing receiving node.

[0263] The sensing transmitting node can be a base station, and the sensing receiving node can be a user equipment.

[0264] Step S145: The base station sends a sensing signal.

[0265] Base stations can send sensing signals to the target being sensed.

[0266] Step S146: The user equipment receives the sensing signal and performs sensing measurement.

[0267] Step S147: The user equipment sends the first information to the base station.

[0268] Step S148: The base station sends the first information to SF.

[0269] This application Figures 4 to 14 In this context, the signaling involved can be carried by Long Term Evolution Positioning Protocol (LPP), New Radio Positioning Protocol A (NRPPa), Non-Access Stratum (NAS), Radio Resource Control (RRC), Media Access Control Control Element (MAC-CE), and Downlink Control Information (DCI) messages, or by signaling carried by new definitions for future wireless communication systems.

[0270] This application also provides a communication device, including a transceiver module and a processing module. Figure 15 and Figure 16 The diagram illustrates the possible structures of communication devices provided in embodiments of this application. These communication devices can be used to implement the functions of a terminal or base station in the above method embodiments, and thus also achieve the beneficial effects of the above method embodiments. In the embodiments of this application, the communication device may be as follows: Figure 1 The terminal 120 shown can also be as follows: Figure 1 The network device 110 shown can also be a module (such as a chip) applied to a terminal or network device.

[0271] like Figure 15 As shown, the communication device 1300 includes a processing unit 1310 and a transceiver unit 1320. Furthermore, the communication device provided in this embodiment is also used to implement... Figures 4 to 14 The method and its corresponding embodiments are described. The processing unit 1310 can be used to generate first information. The transceiver unit 1320 can be used to receive measurement configuration indication information sent by a second device; the measurement configuration includes: a prediction and measurement configuration; and, at least in part based on the indication information, send first information to the second device, the first information being used to indicate prediction information.

[0272] In one possible implementation, the configuration of the prediction and measurement includes at least one of a first time interval, a second time interval, a third time interval, and a fourth time interval.

[0273] In one possible implementation, the first time interval is used to indicate the time interval of a prediction; the second time interval is used to indicate the time interval between the x-th prediction in a set of at least one consecutive predictions and the x-th prediction in the next set of at least one consecutive predictions, where x is less than or equal to K, and K is the number of times the first time interval occurs within the second time interval; the third time interval is used to indicate the time interval between two adjacent measurements within the second time interval; and the fourth time interval is used to indicate the time interval between the y-th measurement in a set of at least one consecutive measurement and the y-th measurement in the next set of at least one consecutive measurements, where y is less than or equal to P, and P is the number of times the first time interval occurs within the third time interval.

[0274] In one possible implementation, the third time interval is equal to the first time interval, and / or the fourth time interval is equal to the second time interval.

[0275] In one possible implementation, the configuration of the prediction and measurement includes: the number of times the first time interval occurs within the second time interval.

[0276] In one possible implementation, the configuration of the prediction and measurement includes at least one of the following: a prediction timestamp within a second time interval, a prediction time interval within a second time interval, parameters included in the measurement information, indication information for enabling or disabling prediction, transmission time information of the measurement information, auxiliary information for prediction, information on prediction error, and information on prediction based on events.

[0277] In one possible implementation, the information that triggers event-based prediction includes at least one of the following: an indication of whether event-based prediction is enabled, a threshold value for the number of times an event occurs when prediction is made based on the event, the start time of monitoring the event, the duration of monitoring the event, a threshold for prediction error, and the event type.

[0278] In one possible implementation, the transceiver unit 1320 is further configured to: send the predictive capability information of the measurement of the first device to the second device.

[0279] In one possible implementation, the prediction capability information includes at least one of the following: indication information supporting prediction, information on supported prediction types, and time information for prediction processing.

[0280] In one possible implementation, the supported prediction type information includes at least the parameter types included in the measurement information.

[0281] In one possible implementation, the transmission time information of the measurement information includes: the offset of the transmission time of the measurement information carrying prediction information relative to the transmission time of the measurement information not carrying prediction information, or the time offset from receiving the last symbol in the sensing signal to transmitting the measurement information; the auxiliary information for prediction includes: historical measurement information and / or information of the measured target; the information of the prediction error includes: the detection time window of the prediction error, and / or the observation offset value of the prediction error.

[0282] In one possible implementation, the transceiver unit 1320 is further configured to: receive a sensing signal; and, based on the received sensing signal, send the measurement information carrying the prediction information to the second device.

[0283] In one possible implementation, the prediction information includes at least one of the following: prediction information and auxiliary prediction information.

[0284] In one possible implementation, the prediction information includes: predicted values ​​of corresponding measurements at K future time points, where K is a positive integer.

[0285] In one possible implementation, the auxiliary prediction information includes at least one of the following: historical measurement values ​​of corresponding measurements at M historical times, and the motion model of the target being measured, where M is a positive integer.

[0286] In one possible implementation, the transceiver unit 1320 is further configured to: use the first information to indicate the measurement information corresponding to the prediction information.

[0287] In one possible implementation, the first device is a user equipment and the second device is a base station; or the first device is a base station and the second device is a sensing network element of the core network; or the first device is a user equipment and the second device is a sensing network element of the core network.

[0288] In another embodiment, a communication method is provided, which is applied to a communication system including a base station and a terminal. The communication method may include, for example: Figures 2 to 10 The embodiments and corresponding examples are shown.

[0289] It is understood that, in order to implement the functions in the above embodiments, the base station and user equipment include hardware structures and / or software modules corresponding to perform each function. Those skilled in the art should readily recognize that, based on the units and method steps described in conjunction with the embodiments disclosed in this application, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application scenario and design constraints of the technical solution.

[0290] The communication device provided in this application can be used to implement the functions of the base station or terminal device in the methods provided in the above-described embodiments of this application, and therefore can also achieve the beneficial effects of the above-described method embodiments. In the embodiments of this application, the communication device can be the final terminal device.

[0291] In one embodiment, the communication device includes a processor and interface circuitry. The processor and interface circuitry are coupled to each other. It is understood that the interface circuitry can be a transceiver or an input / output interface. Optionally, the communication device may further include a memory for storing instructions executed by the processor, or storing input data required for the processor to execute instructions, or storing data generated after the processor executes instructions.

[0292] When the communication device is used to achieve Figure 2 In the method shown, the processor is used to implement the functions of the above-mentioned processing unit, and the interface circuit is used to implement the functions of the above-mentioned transceiver unit.

[0293] It is understood that the processor in the embodiments of this application may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor may be a microprocessor or any conventional processor.

[0294] The method steps in the embodiments of this application can be implemented in hardware or in software instructions executable by a processor. The software instructions can consist of corresponding software modules, which can be stored in random access memory, flash memory, read-only memory, programmable read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. The storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. Alternatively, the ASIC can reside in a base station or user equipment. The processor and storage medium can also exist as discrete components in the base station or user equipment.

[0295] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of this application are performed entirely or partially. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a network device, a user equipment, or other programmable device. The computer program or instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer program or instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; it can also be an optical medium, such as a digital video optical disc; or it can be a semiconductor medium, such as a solid-state drive. The computer-readable storage medium may be a volatile or non-volatile storage medium, or may include both types of storage media.

[0296] In the various embodiments of this application, unless otherwise specified or in case of logical conflict, the terminology and / or descriptions of different embodiments are consistent and can be referenced by each other. The technical features of different embodiments can be combined to form new embodiments according to their inherent logical relationship.

[0297] In this application, "at least one" means one or more, and "more than one" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. In the textual description of this application, the character " / " generally indicates an "or" relationship between the preceding and following related objects; in the formulas of this application, the character " / " indicates a "division" relationship between the preceding and following related objects. "Including at least one of A, B, and C" can mean: including A; including B; including C; including A and B; including A and C; including B and C; including A, B, and C.

[0298] It is understood that the various numerical designations used in the embodiments of this application are merely for descriptive convenience and are not intended to limit the scope of the embodiments of this application. The order of the process numbers described above does not imply the order of execution; the execution order of each process should be determined by its function and internal logic.

Claims

1. A perception prediction method, characterized in that, Applied to the first device, including: Receive measurement configuration indication information sent by the second device; the measurement configuration includes: prediction and measurement configuration; Based at least in part on the indication information, first information is sent to the second device, the first information being used to indicate prediction information.

2. The method according to claim 1, characterized in that, The configuration of the prediction and measurement includes: the time and location information for prediction and measurement.

3. The method according to claim 2, characterized in that, The predicted and measured time location information includes at least one of a first time interval, a second time interval, a third time interval, and a fourth time interval.

4. The method according to claim 3, characterized in that, The first time interval is used to indicate the time interval of the prediction; the second time interval is used to indicate the time interval between the x-th prediction in a set of at least one consecutive predictions and the x-th prediction in the next set of at least one consecutive predictions, where x is less than or equal to K, and K is the number of times the first time interval occurs within the second time interval; the third time interval is used to indicate the time interval between two adjacent measurements within the second time interval; the fourth time interval is used to indicate the time interval between the y-th measurement in a set of at least one consecutive measurement and the y-th measurement in the next set of at least one consecutive measurements, where y is less than or equal to P, and P is the number of times the third time interval occurs within the fourth time interval.

5. The method according to claim 3 or 4, characterized in that, The third time interval is equal to the first time interval, and / or the fourth time interval is equal to the second time interval.

6. The method according to any one of claims 1 to 5, characterized in that, The configuration of the prediction and measurement includes: the number of times the first time interval occurs within the second time interval.

7. The method according to any one of claims 1 to 6, characterized in that, The configuration of the prediction and measurement includes: a prediction timestamp within a second time interval, a prediction time interval within a second time interval, a predicted quantity, a measured quantity, an indication to enable or disable prediction, a measurement report sending time information, prediction auxiliary information, prediction error information, event-based prediction information, and an indication that the prediction timing and measurement timing are associated.

8. The method according to claim 7, characterized in that, The associated prediction timing and measurement timing are used to indicate that the prediction timing and measurement timing overlap or partially overlap, or that the time interval is less than a configured threshold.

9. The method according to claim 7 or 8, characterized in that, The information associated with the prediction timing and measurement timing is determined at least in part based on protocol predefined conditions, and the prediction and measurement configuration includes condition information of the protocol predefined conditions.

10. The method according to claim 9, characterized in that, The predefined conditions of the protocol include: the time interval between the prediction timing and the measurement timing is less than or equal to a threshold value; the threshold value is dynamically configured through signaling or predefined by the protocol.

11. The method according to claim 8, characterized in that, The information for event-based prediction includes at least one of the following: whether to enable event-based prediction, a threshold value for the number of times an event occurs when making predictions based on events, the start time of the monitored event, the duration of the monitored event, the threshold for prediction error, and the event type.

12. The method according to any one of claims 1 to 11, characterized in that, The method further includes: Send the predictive capability information of the measurement of the first device to the second device.

13. The method according to claim 12, characterized in that, The prediction capability information includes at least one of the following: indication information supporting prediction, information on supported prediction types, and time information for prediction processing.

14. The method according to claim 13, characterized in that, The supported prediction type information includes at least the parameter types included in the measurement information.

15. The method according to claim 8, characterized in that, The transmission time information of the measurement report includes: the offset of the transmission time of the measurement information carrying prediction information relative to the transmission time of the measurement information without prediction information, or the time offset from the last symbol in the received sensing signal to the transmission of the measurement information. The auxiliary information for prediction includes: historical measurement information and / or information about the target being measured; The information regarding the prediction error includes: the detection time window of the prediction error, and / or the observation offset value of the prediction error.

16. The method according to any one of claims 1 to 15, characterized in that, Sending the first information to the second device includes: Receive sensing signals; Based on the received sensing signal, the measurement information carrying the prediction information is sent to the second device.

17. The method according to claim 15 or 16, characterized in that, The prediction information includes at least one of the following: prediction information and auxiliary prediction information.

18. The method according to any one of claims 15 to 17, characterized in that, The prediction information includes: predicted values ​​of corresponding measurements at K future time points, where K is a positive integer.

19. The method according to claim 17, characterized in that, The auxiliary prediction information includes at least one of the following: historical measurement values ​​of corresponding measurements at M historical time points, and motion model of the measured target, where M is a positive integer.

20. The method according to any one of claims 1 to 19, characterized in that, The method further includes: The first information is also used to indicate the measurement information corresponding to the prediction information.

21. The method according to any one of claims 1 to 20, characterized in that, The first device is a user equipment, and the second device is a base station; or The first device is a base station, and the second device is a sensing network element of the core network; or The first device is a user equipment, and the second device is a sensing network element of the core network.

22. A perception prediction method, characterized in that, Applied to a second device, including: Send measurement configuration instruction information to the first device; the measurement configuration includes: prediction and measurement configuration; The first device receives first information, which is sent at least in part based on the indication information, and the first information is used to indicate prediction information.

23. The method according to claim 22, characterized in that, The configuration of the prediction and measurement includes: the time and location information for prediction and measurement.

24. The method according to claim 2, characterized in that, The predicted and measured time location information includes at least one of a first time interval, a second time interval, a third time interval, and a fourth time interval.

25. The method according to claim 24, characterized in that, The first time interval is used to indicate the time interval of the prediction; the second time interval is used to indicate the time interval between the x-th prediction in a set of at least one consecutive predictions and the x-th prediction in the next set of at least one consecutive predictions, where x is less than or equal to K, and K is the number of times the first time interval occurs within the second time interval; the third time interval is used to indicate the time interval between two adjacent measurements within the second time interval; the fourth time interval is used to indicate the time interval between the y-th measurement in a set of at least one consecutive measurement and the y-th measurement in the next set of at least one consecutive measurements, where y is less than or equal to P, and P is the number of times the third time interval occurs within the fourth time interval.

26. The method according to claim 24 or 25, characterized in that, The third time interval is equal to the first time interval, and / or the fourth time interval is equal to the second time interval.

27. The method according to any one of claims 22 to 26, characterized in that, The configuration of the prediction and measurement includes: the number of times the first time interval occurs within the second time interval.

28. The method according to any one of claims 22 to 27, characterized in that, The configuration of the prediction and measurement includes: a prediction timestamp within a second time interval, a prediction time interval within a second time interval, a predicted quantity, a measured quantity, an indication to enable or disable prediction, a measurement report sending time information, prediction auxiliary information, prediction error information, event-based prediction information, and an indication that the prediction timing and measurement timing are associated.

29. The method according to claim 28, characterized in that, The associated prediction timing and measurement timing are used to indicate that the prediction timing and measurement timing overlap or partially overlap, or that the time interval is less than a configured threshold.

30. The method according to claim 28 or 29, characterized in that, The information associated with the prediction timing and measurement timing is determined at least in part based on protocol predefined conditions, and the prediction and measurement configuration includes condition information of the protocol predefined conditions.

31. The method according to claim 30, characterized in that, The predefined conditions of the protocol include: the time interval between the prediction timing and the measurement timing is less than or equal to a threshold value; the threshold value is dynamically configured through signaling or predefined by the protocol.

32. The method according to claim 29, characterized in that, The information for event-based prediction includes at least one of the following: whether to enable event-based prediction, a threshold value for the number of times an event occurs when making predictions based on events, the start time of the monitored event, the duration of the monitored event, the threshold for prediction error, and the event type.

33. The method according to any one of claims 22 to 32, characterized in that, The method further includes: Receive the predictive capability information from the measurement of the first device.

34. The method according to claim 33, characterized in that, The prediction capability information includes at least one of the following: indication information supporting prediction, information on supported prediction types, and time information for prediction processing.

35. The method according to claim 24, characterized in that, The supported prediction type information includes at least the parameter types included in the measurement information.

36. The method according to claim 29, characterized in that, The transmission time information of the measurement report includes: the offset of the transmission time of the measurement information carrying prediction information relative to the transmission time of the measurement information without prediction information, or the time offset from the last symbol in the received sensing signal to the transmission of the measurement information. The auxiliary information for prediction includes: historical measurement information and / or information about the target being measured; The information regarding the prediction error includes: the detection time window of the prediction error, and / or the observation offset value of the prediction error.

37. The method according to any one of claims 22 to 36, characterized in that, Sending the first information to the second device includes: The first device sends a sensing signal; the second device, based on the received sensing signal, sends the measurement information carrying the prediction information to the second device.

38. The method according to claim 36 or 37, characterized in that, The prediction information includes at least one of the following: prediction information and auxiliary prediction information.

39. The method according to claim 37, characterized in that, The prediction information includes: predicted values ​​of corresponding measurements at K future time points, where K is a positive integer.

40. The method according to claim 38, characterized in that, The auxiliary prediction information includes at least one of the following: historical measurement values ​​of corresponding measurements at M historical time points, and motion model of the measured target, where M is a positive integer.

41. The method according to any one of claims 22 to 40, characterized in that, The method further includes: The first information is also used to indicate the measurement information corresponding to the prediction information.

42. The method according to any one of claims 22-41, characterized in that, The first device is a user equipment, and the second device is a base station; or The first device is a base station, and the second device is a sensing network element of the core network; or The first device is a user equipment, and the second device is a sensing network element of the core network.

43. A perception prediction method, characterized in that, Applied to base stations, including: Receive measurement configuration indication information sent by the sensing network element; the measurement configuration includes: prediction and measurement configuration; Receive prediction information sent by sensing network elements based on sensing information.

44. A perception prediction method, characterized in that, Applied to sensing network elements, including: Send measurement configuration indication information to the base station; the measurement configuration includes: prediction and measurement configuration; Predictive information based on sensing information is sent to the base station.

45. A chip system, comprising: Memory, used to store computer programs; and at least one processor; When the at least one processor retrieves and runs a computer program from memory, the communication device equipped with the chip system performs the method according to any one of claims 1 to 21.

46. ​​A terminal device, characterized in that, include: Memory, used to store computer programs; and at least one processor; when the at least one processor calls and runs a computer program from memory, the terminal device causes the terminal device to perform the method of any one of claims 1 to 21.

47. A computer-readable storage medium, characterized in that, The storage medium stores a computer program or instructions, which, when executed by a communication device, implement the method as described in any one of claims 1 to 21.

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