Pinching antenna position matching method and device applied to sensing integrated network

Abstract

The invention discloses a pinching antenna position matching method and device applied to a communication and sensing integrated network, and belongs to the technical field of wireless communication, and the method comprises the steps: obtaining an antenna deployable position set, building a channel model, and determining a communication user data rate demand and a sensing precision demand; based on the communication user data rate demand and the perception precision demand, constructing an optimization model taking the communication user data rate and the perception precision as optimization targets; the optimal pinching antenna position matching subset is explored by using a preset intelligent algorithm, so that the optimal position matching of the pinching antenna in the communication and sensing integrated network is realized, and the communication performance and the sensing precision are maximized. According to the method, the problem of position matching of the pinching antenna activated in the communication and sensing integrated network is solved, the communication performance and the sensing precision are effectively improved, and meanwhile, the utilization efficiency of network resources is optimized. The method is suitable for various communication sensing integrated scenes.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a pinching antenna position matching method and device applied to a synaesthesia integrated network. Background Art

[0002] With the continuous development of wireless communication technology, the new generation of network architecture is gradually evolving from a single function to a multi-functional integration. As a new network architecture that supports both efficient communication and accurate environmental perception, the ISAC (Integrated Sensing and Communication) network has received widespread attention. Its core goal is to achieve efficient collaboration between communication and perception by sharing hardware resources and spectrum resources, to meet the extensive application needs of the future intelligent society in the fields of autonomous driving, smart cities, industrial Internet of Things, etc., and is an important development direction for the next generation of wireless communication systems. Traditional fixed antenna designs are often difficult to adapt to complex scenario requirements. Specifically, the ISAC network faces the following major challenges:

[0003] 1) Efficient use of spectrum resources: Communication and sensing functions share spectrum resources, which requires antennas to have high directivity and flexibility to reduce signal interference and improve resource utilization efficiency.

[0004] 2) Dynamic environment adaptability: In complex dynamic scenarios (such as high-density urban areas), antennas need to be able to dynamically adjust according to environmental changes to ensure communication quality and perception accuracy.

[0005] 3) Collaboration of multi-antenna systems: Synaesthesia integrated networks usually use multi-antenna systems to achieve wider coverage and higher perception resolution. However, the antenna position matching problem in the multi-antenna system directly affects the signal transmission performance and perception accuracy.

[0006] In recent years, flexible antenna systems (such as smart reflective surfaces, fluid antenna systems, and movable antennas) have received widespread attention. Due to their ability to dynamically reconstruct wireless channels, flexible antenna systems can significantly improve performance compared to traditional fixed-position antenna systems. However, in most existing flexible antenna systems, the range of change of antenna positions is usually limited to the wavelength scale, which limits their ability to combat large-scale path losses. In addition, the high cost of many existing flexible antenna systems also limits their application in real-world scenarios. In this context, the pinching antenna system proposed can create new line-of-sight links and / or enhance existing transceiver channels by applying low-cost dielectric materials (such as plastic clothespins) at arbitrary locations on the dielectric waveguide.

[0007] Pinching antenna has the following two significant features:

[0008] Ability to support line-of-sight communications: The use of Pinching antennas can create new line-of-sight transceiver links or enhance existing line-of-sight links. Because the position of the Pinching antenna can be flexibly adjusted over a large range, the Pinching antenna can be easily deployed near the target receiver to establish a strong line-of-sight link.

[0009] Ability to flexibly configure antenna systems: The size of a Pinching antenna system can be increased (or decreased) by simply adding additional Pinchings (or releasing existing Pinchings). In addition, multiple Pinching antennas can be flexibly and cost-effectively applied to one or more waveguides, providing a new path for the implementation of MIMO systems.

[0010] As a new antenna design, pinching antennas have broad application prospects due to their flexible radiation patterns and adaptable layout capabilities. However, existing research lacks a systematic method to dynamically match the position of pinching antennas in synaesthesia networks to give full play to their performance advantages. Summary of the invention

[0011] The present invention provides a pinching antenna position matching method and device applied to a synaesthesia integrated network, so as to solve the technical problem that there is still a lack of a systematic method in existing research to dynamically match the position of the pinching antenna in the synaesthesia integrated network, so as to give full play to its performance advantages.

[0012] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0013] In one aspect, the present invention provides a pinching antenna position matching method applied to a synaesthesia integrated network, the pinching antenna position matching method applied to a synaesthesia integrated network comprising:

[0014] Obtain a set of deployable locations for pinching antennas, establish a channel model, and determine the data rate requirements and perception accuracy requirements of communication users in the synaesthesia integrated network;

[0015] Based on the communication user data rate requirements and perception accuracy requirements, the communication performance, perception accuracy and resource utilization efficiency are comprehensively considered to build an optimization model with communication user data rate and perception accuracy as the optimization objectives;

[0016] The optimization model is solved using a preset intelligent algorithm, and the optimal pinching antenna position matching subset is explored in the set of pinching antenna deployable positions to achieve the optimal position matching of the pinching antenna in the synaesthesia integrated network, thereby maximizing the communication performance and perception accuracy of the synaesthesia integrated network.

[0017] Furthermore, based on the communication user data rate requirement and the perception accuracy requirement, the communication performance, perception accuracy and resource utilization efficiency are comprehensively considered to construct an optimization model with the communication user data rate and perception accuracy as the optimization target, including:

[0018] By setting the weighted coefficients of communication user data rate and perception accuracy, performance trade-offs are made to adapt to different network application requirements, and an optimization model with communication user data rate and perception accuracy as optimization targets is constructed.

[0019] Furthermore, the optimization model is expressed as:

[0020]

[0021] Where S represents the set of deployable locations for pinching antennas; Γ n represents the signal to interference plus noise ratio of the nth perceived target; Γ sen represents the preset threshold of perception accuracy; α is the spherical wave parameter; ψ m represents the position of the mth user; p m represents the power allocated by the base station to the mth user; σ 2 is the noise power; e is a natural constant, whose value is about 2.718; j is an imaginary unit; τ is a preset weight factor; M is the total number of users; N is the number of pinching antennas; λ is the wavelength of the wireless signal; represents the position of the nth pinching antenna; θ n Represents the phase shift of the signal at the nth antenna.

[0022] Furthermore, the intelligent algorithm is a Q-learning algorithm, a particle swarm algorithm or a genetic algorithm.

[0023] On the other hand, the present invention further provides a pinching antenna position matching device applied to a synaesthesia integrated network, the pinching antenna position matching device applied to a synaesthesia integrated network comprising:

[0024] The data initialization module is used to obtain the set of deployable locations for pinching antennas and establish a channel model to determine the data rate requirements and perception accuracy requirements of communication users in the synaesthesia integrated network;

[0025] An optimization model building module is used to build an optimization model with communication user data rate and perception accuracy as optimization targets, taking into account communication performance, perception accuracy and resource utilization efficiency;

[0026] The optimization calculation module is used to solve the optimization model using a preset intelligent algorithm, explore the optimal pinching antenna position matching subset in the pinching antenna deployable position set, achieve the optimal position matching of the pinching antenna in the synaesthesia integrated network, and maximize the communication performance and perception accuracy of the synaesthesia integrated network.

[0027] Furthermore, the optimization model building module is specifically used for:

[0028] By setting the weighted coefficients of communication user data rate and perception accuracy, performance trade-offs are made to adapt to different network application requirements, and an optimization model with communication user data rate and perception accuracy as optimization targets is constructed.

[0029] Furthermore, the optimization model is expressed as:

[0030]

[0031] Where S represents the set of deployable locations for pinching antennas; Γ n represents the signal to interference plus noise ratio of the nth perceived target; Γ sen represents the preset threshold of perception accuracy; α is the spherical wave parameter; ψ m represents the position of the mth user; p m represents the power allocated by the base station to the mth user; σ 2 is the noise power; e is a natural constant, whose value is about 2.718; j is an imaginary unit; τ is a preset weight coefficient; M is the total number of users; N is the number of pinching antennas; λ is the wavelength of the wireless signal; represents the position of the nth pinching antenna; θ n Represents the phase shift of the signal at the nth antenna.

[0032] Furthermore, the intelligent algorithm is a Q-learning algorithm, a particle swarm algorithm or a genetic algorithm.

[0033] On the other hand, the present invention further provides an electronic device, comprising a processor and a memory; wherein the memory stores at least one instruction, and the instruction is loaded and executed by the processor to implement the above method.

[0034] In yet another aspect, the present invention further provides a computer-readable storage medium, wherein at least one instruction is stored in the storage medium, and the instruction is loaded and executed by a processor to implement the above method.

[0035] The beneficial effects brought about by the technical solution provided by the present invention include at least:

[0036] The pinching antenna position matching scheme provided by the present invention takes into account the set of antenna deployable positions and the communication and perception performance requirements; constructs an optimization model with the communication user data rate and perception accuracy as the target, sets constraints such as the number of activated antennas and interference control; uses an intelligent algorithm to explore the optimal subset of activated antennas in a discrete position set, outputs the optimal set of activated antennas and their deployment positions, and achieves the optimal position matching of pinching antennas in the synaesthesia integrated network, thereby maximizing communication performance and perception accuracy. It solves the problem of position matching of activated pinching antennas in the synaesthesia integrated network, effectively improves communication performance and perception accuracy, and optimizes the utilization efficiency of network resources. It is applicable to a variety of synaesthesia integrated scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0038] Figure 1 It is a position model of the synaesthesia integrated network and the pinching antenna provided in an embodiment of the present invention;

[0039] Figure 2 It is a schematic diagram of the execution flow of a pinching antenna position matching method applied to a synaesthesia integrated network provided by an embodiment of the present invention;

[0040] Figure 3 It is a system block diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0041] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0042] First of all, it should be noted that in the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "exemplarily" is intended to present the concept in a concrete way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0043] First embodiment

[0044] This embodiment provides a pinching antenna position matching method applied to a synaesthesia integrated network. For a scenario where the set of deployable positions of pinching antennas is known, a solution to the discrete pinching antenna position optimization problem is proposed, which is used to match users and perception targets in an intelligent way according to the known antenna position set, thereby determining the optimal set of activated antennas to maximize the communication and perception performance of the integrated network. The position model of the synaesthesia integrated network and the pinching antenna is as follows: Figure 1 As shown, the execution flow of this method is as follows Figure 2 Specifically, the method comprises the following steps:

[0045] S1, data initialization: obtain the set of antenna deployable positions, analyze the reconstructed link channel model of the pinching antenna, and the communication user data rate requirements and perception accuracy requirements.

[0046] Specifically, in this embodiment, according to the pinching antenna, the line-of-sight transmission link is enhanced or reconstructed through its leakage characteristics, and the pinching antenna link channel model is established; the discrete antenna position set and the communication and perception performance requirements are comprehensively considered, and the state is initialized. Assume that the positions of user n and the activated antenna l are: ψ n =(x n ,y n ,0), For L potential antennas, using the spherical wave channel model, α is the spherical wave parameter, the channel vector of user n can be expressed as:

[0047]

[0048] According to the channel status, analyze the communication and perception performance requirements and initialize the status.

[0049] S2, build optimization model: Based on the communication user data rate requirements and perception accuracy requirements, the optimization model is built by comprehensively considering the communication performance, perception accuracy and resource utilization efficiency;

[0050] Specifically, in this embodiment, by comprehensively analyzing the communication data rate and perception accuracy performance expressions, weighted coefficients of the communication data rate and perception accuracy are set to perform performance trade-offs to adapt to different network application requirements, and a synaesthesia integrated network collaborative optimization target that introduces weighted coefficients is constructed.

[0051] Assume that in time slot t, the location set of pinching antennas is The position ψ of user m m =(x m ,y m ,0), the power allocated by the base station to user m is p m , σ 2 is the noise power, κ is the spherical radiation parameter, and under the action of multiple activated antennas, the data rate of user m is:

[0052]

[0053] Wherein, e represents a natural constant, whose value is approximately 2.718; j represents an imaginary unit; τ represents a weight factor; M represents the total number of users; N represents the number of pinching antennas; λ represents the wavelength of the wireless signal; represents the position of the nth pinching antenna; θ n Represents the phase shift of the signal at the nth antenna.

[0054] While optimizing the user data rate, the signal to interference plus noise ratio (SINR) Γ of the perception target n is considered n , the threshold of perceptual accuracy Γ sen , the weighted synaesthesia performance optimization goal is:

[0055]

[0056] S3, optimization calculation: Use intelligent algorithms to explore the optimal activated antenna subset in the discrete position set of pinching antennas, and maximize the joint optimization goal by dynamically adjusting the antenna selection state.

[0057] The optimization goal is to optimize the synaesthesia performance of discrete decision variables by weighted optimization. An intelligent algorithm is used to explore the optimal subset of activated antennas in the discrete position set of pinching antennas, and the weighted optimization goal is maximized by dynamically adjusting the position of the activated antenna. The intelligent algorithm can be a Q-learning algorithm, a particle swarm algorithm, or a genetic algorithm, which has stronger adaptability and flexibility for discrete position matching optimization goals.

[0058] In summary, this embodiment provides a pinching antenna position matching method applied to a synaesthesia integrated network, which takes into account the set of antenna deployable positions and the communication and perception performance requirements; constructs an optimization model with communication user data rate and perception accuracy as the target, sets constraints such as the number of activated antennas and interference control; uses an intelligent algorithm to explore the optimal subset of activated antennas in a discrete position set, outputs the optimal set of activated antennas and their deployment positions, and achieves the optimal position matching of pinching antennas in the synaesthesia integrated network, thereby maximizing communication performance and perception accuracy. It solves the problem of position matching of activated pinching antennas in a synaesthesia integrated network, effectively improves communication performance and perception accuracy, and optimizes the utilization efficiency of network resources. It is applicable to a variety of synaesthesia integrated scenarios.

[0059] Second embodiment

[0060] This embodiment provides a pinching antenna position matching device applied to a synaesthesia integrated network, and the pinching antenna position matching device applied to a synaesthesia integrated network includes the following modules:

[0061] The data initialization module is used to obtain the set of deployable locations for pinching antennas and establish a channel model to determine the data rate requirements and perception accuracy requirements of communication users in the synaesthesia integrated network;

[0062] An optimization model building module is used to build an optimization model with communication user data rate and perception accuracy as optimization targets, taking into account communication performance, perception accuracy and resource utilization efficiency;

[0063] The optimization calculation module is used to solve the optimization model using a preset intelligent algorithm, explore the optimal pinching antenna position matching subset in the pinching antenna deployable position set, achieve the optimal position matching of the pinching antenna in the synaesthesia integrated network, and maximize the communication performance and perception accuracy of the synaesthesia integrated network.

[0064] Among them, it should be noted that the pinching antenna position matching device applied to the synaesthesia integrated network of this embodiment corresponds to the pinching antenna position matching method applied to the synaesthesia integrated network of the above-mentioned first embodiment; wherein, the functions implemented by each functional module in the pinching antenna position matching device applied to the synaesthesia integrated network of this embodiment correspond one-to-one to each process step in the pinching antenna position matching method applied to the synaesthesia integrated network of the above-mentioned first embodiment; therefore, they will not be repeated here.

[0065] Third embodiment

[0066] This embodiment provides an electronic device, such as Figure 3 As shown, the electronic device includes: a processor and a memory; wherein the processor and the memory can be connected via a communication bus; the memory stores at least one instruction, and the instruction is loaded and executed by the processor to implement the method of the first embodiment. In addition, the electronic device may also include a transceiver, the processor and the transceiver can be connected via a communication bus, and the transceiver is used to communicate with other devices.

[0067] Next, combine Figure 3 The following is a detailed introduction to the various components of the electronic device:

[0068] Among them, the processor is the control center of the electronic device, and the electronic device may include multiple processors, each of which may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The processor here may be a processor or a general term for multiple processing elements. For example, the processor is one or more central processing units (CPUs), or other general-purpose processors, application specific integrated circuits (ASICs), or one or more integrated circuits configured to implement an embodiment of the present invention, such as one or more microprocessors (digital signal processors, DSPs), or one or more field programmable gate arrays (field programmable gate arrays, FPGAs), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor may execute various functions of the electronic device by running or executing software programs stored in the memory and calling data stored in the memory.

[0069] In a specific implementation, as an embodiment, the processor may include one or more CPUs, such as Figure 3 The CPU0 and CPU1 shown in the figure are, of course, only exemplary.

[0070] The memory is used to store the software program for executing the solution of the present invention, and the execution is controlled by the processor. The specific implementation method can refer to the above method embodiment, which will not be repeated here.

[0071] Optionally, the memory may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory may be integrated with the processor or exist independently and accessed through the interface circuit ( Figure 3 (not shown) is coupled to the processor, which is not specifically limited in this embodiment of the present invention.

[0072] The transceiver may include a receiver and a transmitter ( Figure 3 The receiver is used to implement the receiving function, and the transmitter is used to implement the sending function. The transceiver can be integrated with the processor or exist independently and communicate with the electronic device through the interface circuit ( Figure 3 (not shown) is coupled to the processor, which is not specifically limited in this embodiment of the present invention.

[0073] In addition, it should be noted that Figure 3 The structure of the electronic device shown in the figure does not constitute a limitation on the device, and the actual device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. In addition, the technical effects achieved by the electronic device when executing the method of the first embodiment above can refer to the technical effects described in the first embodiment above, so they are not repeated here.

[0074] Fourth embodiment

[0075] This embodiment provides a computer-readable storage medium, which stores at least one instruction, and the instruction is loaded and executed by a processor to implement the method of the first embodiment. The computer-readable storage medium may be a ROM, a random access memory, a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc. The instructions stored therein may be loaded by a processor in a terminal to execute the method.

[0076] In addition, it should be noted that the present invention can be provided as a method, an apparatus or a computer program product. Therefore, the embodiment of the present invention can be in the form of a full or partial hardware embodiment, a full or partial software embodiment or an embodiment combining software and hardware. Moreover, when implemented using software, the embodiment of the present invention can be in the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program codes. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center containing one or more available media sets. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a DVD), or a semiconductor medium. The semiconductor medium may be a solid state hard disk.

[0077] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0078] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device, so that a series of operation steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable terminal device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0079] It should also be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements. In addition, the term "and / or" is only an association relationship describing the associated objects, indicating that there can be three relationships, for example, A and / or B, which can represent: A exists alone, A and B exist at the same time, and B exists alone, wherein A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding. "At least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can be represented by: a, b, c, ab, ac, bc or abc, where a, b, c can be single or plural.

[0080] In addition, it can be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0081] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0082] In several embodiments provided by the present invention, it should be understood that the disclosed equipment, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of functional modules / units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The unit described as a separate component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, it may be located in one place, or it may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, each functional unit in each embodiment of the present invention can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0083] If the method is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0084] Finally, it should be noted that the above is only a preferred embodiment of the present invention. It should be pointed out that although the preferred embodiment of the present invention has been described, for ordinary technicians in this technical field, once the basic creative concept of the present invention is known, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the protection scope of the present invention. Therefore, the attached claims are intended to be interpreted as including the preferred embodiment and all changes and modifications that fall within the scope of the embodiments of the present invention.

Claims

1. A pinching antenna position matching method applied to a synaesthesia integrated network, characterized in that: The pinching antenna position matching method applied to the synaesthesia integrated network includes: Obtain a set of deployable locations for pinching antennas, establish a channel model, and determine the data rate requirements and perception accuracy requirements of communication users in the synaesthesia integrated network; Based on the communication user data rate requirements and perception accuracy requirements, the communication performance, perception accuracy and resource utilization efficiency are comprehensively considered to build an optimization model with communication user data rate and perception accuracy as the optimization objectives; The optimization model is solved using a preset intelligent algorithm, and the optimal pinching antenna position matching subset is explored in the set of pinching antenna deployable positions to achieve the optimal position matching of the pinching antenna in the synaesthesia integrated network, thereby maximizing the communication performance and perception accuracy of the synaesthesia integrated network.

2. The pinching antenna position matching method for a synaesthesia integrated network according to claim 1, characterized in that: The optimization model based on the communication user data rate requirement and the perception accuracy requirement is constructed by comprehensively considering the communication performance, perception accuracy and resource utilization efficiency and taking the communication user data rate and the perception accuracy as the optimization target, including: By setting the weighted coefficients of communication user data rate and perception accuracy, performance trade-offs are made to adapt to different network application requirements, and an optimization model with communication user data rate and perception accuracy as optimization targets is constructed.

3. The pinching antenna position matching method for a synaesthesia integrated network according to claim 2, characterized in that: The optimization model is expressed as: Where S represents the set of deployable locations for pinching antennas; Γ n represents the signal to interference plus noise ratio of the nth perceived target; Γ sen represents the preset threshold of perception accuracy; α is the spherical wave parameter; ψ m represents the position of the mth user; p m represents the power allocated by the base station to the mth user; σ 2 is the noise power; e is a natural constant; j is an imaginary unit; τ is a preset weight factor; M is the total number of users; N is the number of pinching antennas; λ is the wavelength of the wireless signal; ψ n Pin represents the position of the nth pinching antenna; θ n Represents the phase shift of the signal at the nth antenna.

4. The pinching antenna position matching method for a synaesthesia integrated network according to claim 1, characterized in that: The intelligent algorithm is a Q-learning algorithm, a particle swarm algorithm or a genetic algorithm.

5. A pinching antenna position matching device applied to a synaesthesia integrated network, characterized in that: The pinching antenna position matching device applied to the synaesthesia integrated network comprises: The data initialization module is used to obtain the set of deployable locations for pinching antennas and establish a channel model to determine the data rate requirements and perception accuracy requirements of communication users in the synaesthesia integrated network; An optimization model building module is used to build an optimization model with communication user data rate and perception accuracy as optimization targets, taking into account communication performance, perception accuracy and resource utilization efficiency; The optimization calculation module is used to solve the optimization model using a preset intelligent algorithm, explore the optimal pinching antenna position matching subset in the pinching antenna deployable position set, achieve the optimal position matching of the pinching antenna in the synaesthesia integrated network, and maximize the communication performance and perception accuracy of the synaesthesia integrated network.

6. The pinching antenna position matching device for synaesthesia integrated network according to claim 5, characterized in that: The optimization model building module is specifically used for: By setting the weighted coefficients of communication user data rate and perception accuracy, performance trade-offs are made to adapt to different network application requirements, and an optimization model with communication user data rate and perception accuracy as optimization targets is constructed.

7. The pinching antenna position matching device for synaesthesia integrated network according to claim 6, characterized in that: The optimization model is expressed as: Where S represents the set of deployable locations for pinching antennas; Γ n represents the signal to interference plus noise ratio of the nth perceived target; Γ sen represents the preset threshold of perception accuracy; α is the spherical wave parameter; ψ m represents the position of the mth user; p m represents the power allocated by the base station to the mth user; σ 2 is the noise power; e represents a natural constant; j represents an imaginary unit; τ represents a preset weight factor; M represents the total number of users; N represents the number of pinching antennas; λ represents the wavelength of the wireless signal; represents the position of the nth pinching antenna; θ n Represents the phase shift of the signal at the nth antenna.

8. The pinching antenna position matching device for synaesthesia integrated network according to claim 5, characterized in that: The intelligent algorithm is a Q-learning algorithm, a particle swarm algorithm or a genetic algorithm.

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