An ai-based positioning method and device

Abstract

The application discloses an AI-based positioning method, which comprises the following steps: a network entity of a wireless communication system sends, receives or generates first information, and the first information contains a group of base station identifiers as input control information of an AI model. The application also contains a device applying the method. The application solves the problem of how the wireless communication system improves the AI-based positioning performance through auxiliary information transmission, and is especially suitable for a complex scene in which multiple network side devices provide positioning services for a terminal and a large number of obstructions exist.

Description

Technical Field

[0001] This application relates to the field of wireless communication technology, and in particular to an AI-based positioning method and device. Background Technology

[0002] Mobile communication networks contain a vast amount of data resources. Utilizing artificial intelligence (AI) technology to rationally utilize and explore these 5G data resources can effectively improve mobile communication systems. Mobile communication systems face a complex and diverse array of problems, and numerous studies have demonstrated that AI-based algorithms can effectively enhance the performance of both the mobile network and wireless sides. Improving mobile system performance using AI technology has become a major direction for future network design.

[0003] When wireless communication systems employ AI technology to enhance positioning performance, the challenge lies in effectively utilizing the model. AI-based positioning models require comprehensive channel information from the positioning terminal and multiple base stations for location calculation, and training these models also necessitates a large amount of labeled data. To further improve the application scenarios of AI-based positioning algorithms, it is necessary to consider reducing the computational complexity of the AI ​​model, enhancing its generalization ability, and decreasing the training complexity and training dataset size. This invention provides an AI-based positioning method and apparatus that enables more efficient use of AI models to achieve positioning functionality.

[0004] Currently, the 5G standard supports the transmission of positioning signals, but it has not standardized various positioning technologies. There is also no standardization for AI model-based positioning methods. Existing positioning schemes, especially non-AI-based ones, require obtaining distance information from the terminal to three base stations and location information from those three base stations before calculating the terminal's location. When multiple base station information can be used for calculation, selection from the three base stations is required to complete the positioning calculation. The device performing the positioning typically determines the selection of the three base stations based on the strength of the positioning signal, which is not directly supported by the standard. However, to support AI-based positioning, the base station selection is not limited to three; information from any number of base stations can be used as input to the AI ​​model, and the input information is no longer limited to distance information. Summary of the Invention

[0005] This application proposes an AI-based positioning method and device to address the problem of how wireless communication systems can improve AI-based positioning performance through auxiliary information transmission. It is particularly suitable for complex scenarios where multiple network-side devices provide positioning services for a single terminal and where there are numerous obstructions.

[0006] Firstly, this application proposes an AI-based positioning method for use in wireless communication systems, comprising the following steps:

[0007] Send, receive, or generate first information, the first information containing a set of base station identifiers as input control information for the AI ​​model.

[0008] Preferably, the first information includes indication information associated with each of the base station identifiers, the indication information including the weight corresponding to each of the base station identifiers and / or the AI ​​model identifier corresponding to each of the base station identifiers.

[0009] Furthermore, the first information is carried in at least one of the following ways:

[0010] Downlink control information (DCI) in the downlink control channel (PDCCH); higher layer information in the downlink shared channel (PDSCH); higher layer information in the uplink shared channel (PUSCH).

[0011] When the downlink information includes the first information, the downlink information also includes the second information; the second information includes AI model information and / or AI dataset information.

[0012] Preferably, the first information is carried by the PDCCH, and the second information is carried by the PDSCH. The downlink control information carrying the first information also includes indication information of the location of the second information.

[0013] Alternatively, preferably, the first and second information are carried by the PDSCH, and the downlink control information includes indication information of the first information location and indication information of the second information location.

[0014] When the uplink information includes the first information, the downlink information includes the third information. The third information includes information on the transmission time, transmission frequency resource indication, and number of elements transmitted for the first information.

[0015] Secondly, this application also proposes a network-side device for implementing the method described in any one of the first aspects, comprising a network determination module and a network transmission module;

[0016] The network determination module is used to determine the first information.

[0017] The network sending module is used to send the first information.

[0018] Alternatively, as another embodiment of the second aspect of this application, a network-side device is provided for implementing the method of any one of the claims, comprising a network receiving module and a network determining module;

[0019] The network receiving module is used to receive the first information;

[0020] The network determination module is used to determine the first information and execute the AI ​​model.

[0021] Thirdly, this application also proposes a terminal-side device for implementing the method described in any embodiment of this application, comprising a terminal receiving module and a terminal determining module;

[0022] The terminal receiving module is used to receive the first information;

[0023] The terminal determination module is used to determine the first information and execute the AI ​​model.

[0024] As another embodiment of the third aspect of this application, a terminal-side device includes a terminal determination module, a terminal sending module, and the terminal receiving module;

[0025] The terminal determination module is used to determine the first information;

[0026] The terminal sending module allows the user to send the first information.

[0027] The terminal receiving module is used to receive downlink third information, which includes information on the transmission time of the first information, transmission frequency resource indication information, and transmission element quantity indication information.

[0028] Fourthly, this application also proposes a communication device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the method as described in any embodiment of the first aspect of this application.

[0029] Fifthly, this application also proposes a computer-readable medium on which a computer program is stored, which, when executed by a processor, implements the steps of the method described in any embodiment of the first aspect of this application.

[0030] Sixthly, this application also proposes a mobile communication system comprising at least one network-side device as described in any embodiment of this application and / or at least one terminal-side device as described in any embodiment of this application.

[0031] The above-described technical solutions adopted in the embodiments of this application can achieve the following beneficial effects:

[0032] The method and apparatus provided by this invention enable dynamic input control of the AI ​​model through the interaction of the first information between the network side and the terminal side. The input control involves the selection of the base station and the weights of its related inputs. By controlling the quantity and quality of the AI ​​model inputs, the computational complexity of the model is reduced, and the inference accuracy is improved. This invention can also support network or terminal-side devices to use multiple AI models simultaneously for positioning, effectively expanding the application scope of AI-based positioning solutions. Attached Figure Description

[0033] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0034] Figure 1 This is a schematic diagram of an embodiment of the first information of the method of this application;

[0035] Figure 2 A schematic diagram illustrating an embodiment of the first information application;

[0036] Figure 3 This is a flowchart of another embodiment of the method of the first information in this application;

[0037] Figure 4 This is a schematic diagram of another embodiment of the first information application;

[0038] Figure 5 A schematic diagram of a third embodiment of the method of this application;

[0039] Figure 6 This is a schematic diagram illustrating the joint transmission of the first and second information via PDCCH / PDSCH.

[0040] Figure 7 A schematic diagram of the transmission of the first and second information via PDSCH;

[0041] Figure 8 A schematic diagram illustrating the sending of first information from a terminal device to a network device.

[0042] Figure 9 This is a schematic diagram of an embodiment of a network-side device;

[0043] Figure 10 This is a schematic diagram of an embodiment of the terminal-side device;

[0044] Figure 11 This is a schematic diagram of the structure of a network-side device according to another embodiment of the present invention;

[0045] Figure 12 This is a block diagram of a terminal-side device according to another embodiment of the present invention. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0047] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0048] AI-based positioning technologies can be divided into direct positioning and indirect positioning. For direct positioning, the wireless channel information between the terminal and multiple base stations can be used as input to the AI ​​model, which then obtains the terminal's location information. For indirect positioning, the AI ​​model can calculate key variables between different base stations and the terminal, improving variable accuracy and thus aiding in the final accurate location calculation.

[0049] Different positioning algorithms require consideration of AI model complexity and output accuracy. When multiple base stations exist within an area for terminal positioning, the channel information between these base stations and the terminal is used as input to the AI ​​model for positioning-related calculations. Using too much base station channel information as AI model input increases inference complexity and significantly raises the complexity, training overhead, and required labeled data volume of the AI ​​model to support a large amount of input information. This invention supports precise control of the AI ​​model's input from the terminal or network side through signal transmission, thereby enabling efficient use of the AI ​​model for positioning.

[0050] The solution defined in this application is as follows: the first information includes at least one set of base station identifiers (IDs). The first information may also include indication information associated with the base station identifiers, including but not limited to reliability information (i.e., weights) and model identifier (model ID) information corresponding to each base station identifier.

[0051] This application proposes an AI-based positioning method. A first information is transmitted, received, or generated by a network entity in a wireless communication system. This first information includes a set of base station identifiers, serving as input control information for an AI model. The first information is determined, and it includes indication information associated with each base station identifier. This indication information includes a weight corresponding to each base station identifier and / or an AI model identifier corresponding to each base station identifier.

[0052] The first information can be carried by DCI (downlink control information) carried by PDCCH;

[0053] The first information can be carried by higher-level information carried by PDSCH;

[0054] The first information can be carried by higher-level information carried by PUSCH.

[0055] Utilizing AI models for direct or auxiliary positioning requires a series of information exchanges to ensure the efficient use of the AI ​​models. This invention primarily manages and controls the number of base stations inputting into the AI ​​model by transmitting the first piece of information. This first piece of information can be transmitted in several ways:

[0056] Method 1: The network-side device transmits the first information to the terminal-side device; for example, via PDCCH or PDSCH.

[0057] Method 2: The network-side device transmits the first information and the second information, along with other related information, to the terminal-side device.

[0058] The first information, along with the second information, can be sent to the terminal via the network. The second information is AI model information and / or dataset information. The AI ​​model information includes model structure information, parameter information, input and output descriptions, etc. The dataset information includes data that can be used for AI model training or testing.

[0059] Specifically, the first information and the second information are jointly carried by PDCCH and PDSCH, with the first information carried by PDCCH and the second information carried by PDSCH. Alternatively, the first information and the second information are carried together by PDSCH.

[0060] Method 3: The terminal-side device transmits the first information to the network-side device.

[0061] The terminal-side device can transmit the first information on the PUSCH based on the third information sent by the network-side device. The third information may include an indication of the transmission time of the first information, the transmission frequency resource, and the number of transmission elements.

[0062] After receiving the first information, the terminal-side device or the network-side device adjusts the input to the AI ​​model based on the base station information provided in the first information. This adjustment may include selecting the number of input pieces for the AI ​​model based on the first information, or adding weight information to different input pieces.

[0063] In the following Figures 1-8 In the embodiments:

[0064] When the downlink information includes the first information, the downlink information may also include the second information; the second information includes AI model information and / or AI dataset information.

[0065] Preferably, the first information is carried by the PDCCH, and the second information is carried by the PDSCH. The downlink control information carrying the first information also includes indication information of the location of the second information.

[0066] Alternatively, preferably, the first and second information are carried by the PDSCH, and the downlink control information includes indication information of the first information location and indication information of the second information location.

[0067] When the uplink information includes the first information, the downlink information includes the third information. The third information includes the transmission time of the first information, transmission frequency resource indication information, and transmission element quantity indication information.

[0068] Figure 1 This is a schematic diagram of an embodiment of the first information of the method of this application.

[0069] In this embodiment, the network-side device sends the first information via PDCCH. The first information contains multiple fields, each representing a different base station identifier. The first information can be sent via DCI information carried by PDCCH or via higher-layer information carried by PDSCH. As shown in the figure, the first information contains ID information from five base stations, indicating that the network recommends that the terminal use measurements from these five base stations as input to the AI ​​model.

[0070] Figure 2 This is a schematic diagram of an embodiment of the first information application.

[0071] On the terminal side, based on the first information, the channel information from the five base stations indicated by the first information to the terminal is used as the input to the AI ​​model to obtain the terminal's location information.

[0072] Figure 3 This is a flowchart of another embodiment of the method of the present application.

[0073] In this embodiment, the network-side device sends the first information via PDCCH. The first information includes a base station identifier and the corresponding usage weight value. The first information can be sent by DCI information carried by PDCCH or by higher-layer information carried by PDSCH. As shown in the figure, the first information includes the ID information and corresponding weight information of five base stations, representing the network's recommendation for the terminal to use measurements from the above five base stations, and assigning weights to the base station usage as input to the AI ​​model.

[0074] Figure 4This is a schematic diagram of another embodiment of the first information application.

[0075] On the terminal side, such as Figure 4 As shown, based on the first information, the channel information from the five base stations to the terminal and the corresponding weights indicated by the first information are used as inputs to the AI ​​model to obtain the terminal's location information.

[0076] Figure 5 A schematic diagram of the third embodiment of the first information of this application method.

[0077] In this embodiment, the network-side device sends the first information via PDCCH. This embodiment is relative to... Figures 1-2 The main difference in the illustrated embodiment is that the first information includes a terminal applicable model ID indicator. This AI model ID indicates the applicable model when the terminal has multiple available AI models. The specific design of the first information is as follows: Figure 5 As shown.

[0078] Figure 6 This is a schematic diagram of the joint transmission of the first and second information in PDCCH / PDSCH.

[0079] In this embodiment, the first information and the second information are sent together. The content of the first information is the same as in Embodiment 1, and it is transmitted by the DCI carried by the PDCCH. The DCI that transmits the first information simultaneously indicates the PDSCH channel carrying the second information. The second information is the specific information of the AI ​​model used by the terminal, including the structure of the AI ​​model and the parameter information in the AI ​​model. A typical AI model structure includes the number and arrangement of neurons, and typical parameters include the values ​​of various coefficients in each neuron. Figure 6 A schematic diagram is provided showing the joint transmission of the first and second information using PDCCH and PDSCH.

[0080] Figure 7 This is a schematic diagram of the transmission of the first and second information via PDSCH.

[0081] In this embodiment, the first information and the second information are sent together. The content of the first information is the same as in Embodiment 1, and it is sent together with the second information by the PDSCH. The DCI information carried by the PDCCH indicates the location of the first information and the second information. After receiving the information, the terminal performs positioning based on an AI model, the same as in Embodiments 1 and 2. Figure 7 A schematic diagram is provided showing the joint transmission of the first and second information using PDSCH.

[0082] Figure 8This is a schematic diagram illustrating the process of a terminal device sending its first message to a network device.

[0083] In this embodiment, the first information is sent from the terminal to the network side. The content of the first information is as described in Embodiment 1. Figure 5 As shown. The first information sent by the terminal to the base station needs to be carried by the PUSCH. The PUSCH carrying the first information needs to be indicated by the network-side device through the third information (DCI information carried by the PDCCH), and this process is as follows. Figure 8 As shown.

[0084] Figure 9 This is a schematic diagram of an embodiment of a network-side device.

[0085] This application also proposes a network-side device. Using the method of any embodiment of this application, at least one module of the network-side device is configured to: determine, generate, or transmit the first information, the second information, the indication information of the location of the first information, the indication information of the location of the second information, and the third information during downlink; and receive and determine the first information during uplink.

[0086] To implement the above technical solution, this application also proposes a network-side device 400, which includes a network determination module 402 and a network transmission module 401 connected to each other.

[0087] The network determination module is used to determine the first information.

[0088] The network transmission module is used to transmit the first information, which is carried by the PDCCH and the second information is carried by the PDSCH. The downlink control information carrying the first information also includes indication information of the location of the second information. Alternatively, preferably, the first and second information are carried by the PDSCH, and the downlink control information includes indication information of the location of the first and second information.

[0089] Alternatively, as another embodiment of this application, a network-side device includes a network transmitting module 401, a network receiving module 403, and a network determining module 402 that are interconnected.

[0090] The network sending module is used to send the third information;

[0091] The network receiving module is used to receive the first information according to the location and number of elements of the PUSCH resource indicated by the third information.

[0092] The network determination module is used to determine the first information and execute the AI ​​model.

[0093] The network-side device adjusts the input to the AI ​​model based on the base station information provided by the first information. This adjustment may include selecting the number of input pieces for the AI ​​model based on the first information, or adding weight information to different input pieces.

[0094] The specific methods for implementing the functions of the network sending module, network determining module, and network receiving module are as described in the various method embodiments of this application, and will not be repeated here.

[0095] Figure 10 This is a schematic diagram of an embodiment of the terminal-side device.

[0096] This application also proposes a terminal-side device using the method of any embodiment of this application, wherein at least one module in the terminal-side device is configured to perform at least one of the following functions: During downlink, receiving and determining the first information, the second information, an indication of the location of the first information, an indication of the location of the second information, and the third information; During uplink, determining and sending the first information.

[0097] To implement the above technical solution, this application proposes a terminal-side device 500, which includes a terminal receiving module 503 and a terminal determining module 502 connected to each other.

[0098] The terminal receiving module is used to receive the first information and the second information;

[0099] The terminal determination module is used to determine the first information and execute the AI ​​model.

[0100] Based on the base station information provided by the first piece of information, the input to the AI ​​model is adjusted. This adjustment may include selecting the number of input pieces for the AI ​​model based on the first piece of information, or adding weight information to different input pieces.

[0101] As another embodiment of this application, this application proposes a terminal-side device, including a terminal determination module 502, a terminal sending module 501, and a terminal receiving module 503 that are interconnected.

[0102] The terminal determination module is used to determine the first information;

[0103] The terminal sending module is used to send the first information according to the PUSCH resource location and element quantity indicated by the third information;

[0104] The terminal receiving module is used to receive downlink third information, which includes information on the transmission time of the first information, transmission frequency resource indication information, and transmission element quantity indication information.

[0105] The specific methods for implementing the functions of the terminal sending module, the terminal determining module, and the terminal receiving module are as described in the various method embodiments of this application, and will not be repeated here.

[0106] The terminal-side equipment described in this application may refer to user equipment (UE) or personal mobile terminal equipment.

[0107] Figure 11 A schematic diagram of a network-side device according to another embodiment of the present invention is shown. As shown, the network-side device 600 includes a processor 601, a wireless interface 602, and a memory 603. The wireless interface may consist of multiple components, including a transmitter and a receiver, providing a unit for communication with various other devices over a transmission medium. The wireless interface implements communication functions with the terminal-side device, processes wireless signals through receiving and transmitting devices, and the data carried by the signals is communicated with the memory or processor via an internal bus structure. The memory 603 contains a computer program that executes any embodiment of this application, and the computer program runs or modifies the processor 601. The memory, processor, and wireless interface circuit are connected via a bus system. The bus system includes a data bus, a power bus, a control bus, and a status signal bus, which will not be described in detail here.

[0108] Figure 12 This is a block diagram of a terminal-side device according to another embodiment of the present invention. The terminal-side device 700 includes at least one processor 701, a memory 702, a user interface 703, and at least one network interface 704. The various components in the terminal-side device 700 are coupled together via a bus system. The bus system is used to implement communication between these components. The bus system includes a data bus, a power bus, a control bus, and a status signal bus.

[0109] User interface 703 may include a display, keyboard, or clicking device, such as a mouse, trackball, touchpad, or touchscreen.

[0110] The memory 702 stores executable modules or data structures. The memory may store an operating system and application programs. The operating system includes various system programs, such as a framework layer, core library layer, and driver layer, used to implement various basic business functions and handle hardware-based tasks. The application programs include various applications, such as media players and browsers, used to implement various application functions.

[0111] In an embodiment of the present invention, the memory 702 contains a computer program that executes any embodiment of the present application, the computer program being run on or modified by the processor 701.

[0112] The memory 702 includes a computer-readable storage medium. The processor 701 reads the information in the memory 702 and, in conjunction with its hardware, completes the steps of the above-described method. Specifically, the computer-readable storage medium stores a computer program, which, when executed by the processor 701, implements the steps of the method embodiments described in any of the above embodiments.

[0113] The processor 701 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the method in this application can be completed by the integrated logic circuitry in the hardware of the processor 701 or by instructions in software form. The processor 701 may be a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a readily available programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of this invention can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor.

[0114] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. In a typical configuration, the device of this application includes one or more processors (CPUs), an input / output user interface, a network interface, and memory.

[0115] Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0116] Therefore, this application also proposes a computer-readable medium storing a computer program that, when executed by a processor, implements the steps of the method described in any embodiment of this application. For example, the memory 603, 702 of the present invention may include non-permanent memory in the form of computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM.

[0117] based on Figures 9-12 In addition to the embodiments described herein, this application also proposes a mobile communication system comprising at least one embodiment of any terminal-side device described herein and / or at least one embodiment of any network-side device described herein.

[0118] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0119] It should also be noted that the terms "first", "second", etc. in this application are used to distinguish multiple objects with the same name, and unless otherwise specified, they have no meaning of order or size.

[0120] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. An AI-based positioning method, characterized in that, Includes the following steps: Send, receive, or generate first information, the first information including a set of base station identifiers and the weights corresponding to the base station identifiers, as input control information for the AI ​​model; When multiple base stations exist in an area that can be used for terminal positioning, the channel information between the multiple base stations and the terminal is used as input to the AI ​​model for positioning-related calculations. The input control information is used to select the number of input information for the AI ​​model and add weight information to different input information to improve inference accuracy.

2. The AI-based positioning method as described in claim 1, characterized in that, The first information includes an AI model identifier corresponding to each of the base station identifiers.

3. The AI-based positioning method as described in claim 1, characterized in that, The first information is carried in at least one of the following ways: Downlink control information (DCI) in the downlink control channel (PDCCH); higher layer information in the downlink shared channel (PDSCH); higher layer information in the uplink shared channel (PUSCH).

4. The AI-based positioning method as described in claim 1, characterized in that, When the downlink information includes the first information, the downlink information also includes the second information; The second information includes AI model information and / or AI dataset information.

5. The AI-based positioning method as described in claim 4, characterized in that, The first information is carried by the PDCCH, and the second information is carried by the PDSCH; The downlink control information carrying the first information also includes indication information of the location of the second information.

6. The AI-based positioning method as described in claim 4, characterized in that, The first and second information are carried by the PDSCH, and the downlink control information includes indication information of the location of the first information and indication information of the location of the second information.

7. The AI-based positioning method as described in claim 1, characterized in that, When the uplink information includes the first information, the downlink information includes the third information; The third information includes the sending time, sending frequency resource indication information, and sending element quantity indication information of the first information.

8. A network-side device for implementing the method according to any one of claims 1 to 6, characterized in that, Includes a network determination module and a network transmission module; The network determination module is used to determine the first information; The network sending module is used to send the first information.

9. A network-side device for implementing the method according to any one of claims 1 to 3 and 7, characterized in that, Includes a network receiving module and a network determination module; The network receiving module is used to receive the first information; The network determination module is used to determine the first information and execute the AI ​​model.

10. A terminal-side device for implementing the method according to any one of claims 1 to 6, characterized in that, Includes a terminal receiving module and a terminal determining module; The terminal receiving module is used to receive the first information; The terminal determination module is used to determine the first information and execute the AI ​​model.

11. A terminal-side device for implementing the method according to any one of claims 1 to 3 and 7, characterized in that, It includes a terminal determination module, a terminal transmission module, and a terminal receiving module; The terminal determination module is used to determine the first information; The terminal sending module is used to send the first information; The terminal receiving module is used to receive downlink third information, which includes information on the transmission time of the first information, transmission frequency resource indication information, and transmission element quantity indication information.

12. A communication device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the method as described in any one of claims 1 to 7.

13. A computer-readable medium storing a computer program that, when executed by a processor, implements the steps of the method as described in any one of claims 1 to 7.

14. A mobile communication system comprising at least one network-side device as described in any one of claims 8 to 9 and at least one terminal-side device as described in any one of claims 10 to 11.

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