Pinching antenna assisted communication and inductance integrated network resource allocation method and device

Abstract

The invention discloses a resource allocation method and device for a Pinching antenna-assisted communication-inductance integrated network, and belongs to the technical field of wireless communication, and the method comprises the steps: analyzing the link characteristics of a Pinching antenna, building a link model, and determining the communication performance demand and the sensing precision demand of the Pinching antenna-assisted communication-inductance integrated network; the method comprises the following steps: establishing a resource allocation model based on a communication performance demand and a sensing precision demand by comprehensively considering the dynamic link adjustment capability of a Pinching antenna; solving the resource allocation model by adopting a multi-objective optimization algorithm, and generating an optimal resource allocation scheme; and dynamically adjusting the position, spectrum and power distribution of the Pinching antenna according to the generated optimal resource distribution scheme so as to ensure that the cooperative performance of communication and sensing functions is optimal. By adopting the scheme provided by the invention, the cooperative performance of the communication and sensing functions of the Pinching antenna-assisted communication and sensing integrated network can be ensured to be optimal.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a Pinching antenna-assisted synaesthesia-integrated network resource allocation method and device. Background Art

[0002] With the rapid development of 6G communication technology in the future, communication networks are gradually evolving towards high frequency bands, large bandwidths, and multiple antennas. In this process, ISAC (Integrated Sensing and Communication) has gradually become a research hotspot. ISAC technology can significantly improve spectrum efficiency, reduce system complexity, and provide innovative solutions for scenarios such as Internet of Vehicles, drone navigation, and smart homes by using the same network resources to achieve efficient communication and environmental perception. In the ISAC system, the coordinated optimization of communication and perception performance and resource allocation are one of the key technologies. Traditional multi-antenna design often requires a trade-off between communication performance and perception performance, and it is difficult to meet the high efficiency requirements of both at the same time. In addition, with the continuous increase in network users and perception tasks, how to dynamically and efficiently allocate resources under limited resources to further improve the overall performance of the system has become an urgent problem to be solved.

[0003] In recent years, flexible antenna systems (including 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 in antenna position is usually limited to the wavelength scale, which limits their ability to combat large-scale path loss. 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. Pinching antennas have the following two significant features:

[0004] 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.

[0005] 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.

[0006] In response to the resource allocation task challenge of the synaesthesia integrated network, Pinching antenna, as a new antenna design technology, provides higher degrees of freedom for the performance optimization of the synaesthesia integrated network with its flexibility and low cost advantages. However, existing research has not fully explored the potential of Pinching antenna in synaesthesia integrated resource allocation. Therefore, a synaesthesia integrated network resource allocation scheme based on Pinching antenna assistance is urgently needed to achieve the coordinated optimization of communication and perception performance, so as to meet the diverse needs in complex scenarios. Summary of the invention

[0007] The present invention provides a Pinching antenna-assisted synaesthesia integrated network resource allocation method and device, so as to solve the technical problem of the current lack of a Pinching antenna-assisted synaesthesia integrated network resource allocation optimization solution.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0009] On the one hand, the present invention provides a Pinching antenna-assisted synaesthesia-integrated network resource allocation method, the Pinching antenna-assisted synaesthesia-integrated network resource allocation method comprising:

[0010] Analyze the link characteristics of the Pinching antenna and establish a link model to determine the communication performance requirements and perception accuracy requirements of the Pinching antenna-assisted synaesthesia integrated network;

[0011] Taking into account the dynamic link adjustment capability of Pinching antennas, a resource allocation model is established based on the communication performance requirements and perception accuracy requirements of the interaceptive integrated network assisted by Pinching antennas.

[0012] Use the preset multi-objective optimization algorithm to solve the resource allocation model and generate the optimal resource allocation plan;

[0013] According to the generated optimal resource allocation plan, the position, spectrum allocation and power allocation of the Pinching antenna are dynamically adjusted to ensure the optimal collaborative performance of communication and perception functions.

[0014] Furthermore, analyzing the link characteristics of the Pinching antenna and establishing a link model includes:

[0015] According to the parameters of the Pinching antenna, the link performance of the Pinching antenna under different transmission distances, positions and signal power conditions is evaluated in a simulation environment; a mathematical link model is established to describe the role of the Pinching antenna in enhancing the signal strength of an existing line-of-sight transmission link or establishing a new transmission link in the absence of line-of-sight.

[0016] Furthermore, the optimization objectives of the resource allocation model include: maximizing the data rate of the communication user, and ensuring that the signal to interference noise ratio of the perception target is greater than a set threshold.

[0017] Furthermore, the resource allocation model is expressed as:

[0018]

[0019] stΓ n ≥λ sen

[0020] in, κ represents the spherical radiation parameter; p m represents the power allocated by the base station to the mth user; represents the position of the pinching antenna; ψ m represents the position of the mth user; σ 2 is the noise power; Γ n represents the signal to interference plus noise ratio of the nth perceived target; ψ pin represents the position vector of the pinching antenna; p represents the transmission power vector; M represents the total number of users; λ sen The signal-to-interference-plus-noise ratio threshold representing the perceived information.

[0021] Furthermore, the multi-objective optimization algorithm is a genetic algorithm, a particle swarm optimization algorithm or a reinforcement learning algorithm.

[0022] Furthermore, the method dynamically adjusts the position, spectrum allocation and power allocation of the Pinching antenna according to the generated optimal resource allocation scheme to ensure the optimal collaborative performance of the communication and perception functions, including:

[0023] According to the generated optimal resource allocation plan, the position, spectrum allocation and power allocation of the Pinching antenna are dynamically adjusted to achieve resource allocation. The resource allocation process is continuously optimized using the reinforcement learning algorithm, so that the collaborative performance of communication and perception functions is always in the optimal state.

[0024] When the user moves or the link quality fluctuates, the resource allocation plan is re-optimized and adjusted in real time.

[0025] On the other hand, the present invention further provides a Pinching antenna-assisted synaesthesia-integrated network resource allocation device, the Pinching antenna-assisted synaesthesia-integrated network resource allocation device comprising:

[0026] Data initialization module, used to analyze the link characteristics of the Pinching antenna and establish a link model, and determine the communication performance requirements and perception accuracy requirements of the interawareness integrated network assisted by the Pinching antenna;

[0027] The resource allocation model building module is used to comprehensively consider the dynamic link adjustment capability of the Pinching antenna and establish a resource allocation model based on the communication performance requirements and perception accuracy requirements of the interaceptive integrated network assisted by the Pinching antenna;

[0028] The model solving module is used to solve the resource allocation model using a preset multi-objective optimization algorithm to generate an optimal resource allocation solution;

[0029] The dynamic adjustment module is used to dynamically adjust the position, spectrum allocation and power allocation of the Pinching antenna according to the generated optimal resource allocation plan to ensure the optimal collaborative performance of communication and perception functions.

[0030] Furthermore, the data initialization module is specifically used for:

[0031] According to the parameters of the Pinching antenna, the link performance of the Pinching antenna under different transmission distances, positions and signal power conditions is evaluated in a simulation environment; a mathematical link model is established to describe the role of the Pinching antenna in enhancing the signal strength of an existing line-of-sight transmission link or establishing a new transmission link in the absence of line-of-sight.

[0032] Further, the optimization objectives of the resource allocation model include: maximizing the data rate of the communication user, and ensuring that the signal to interference noise ratio of the sensing target is greater than a set threshold;

[0033] The resource allocation model is expressed as:

[0034]

[0035] stΓ n ≥λ sen

[0036] in, κ represents the spherical radiation parameter; p m represents the power allocated by the base station to the mth user; represents the position of the pinching antenna; ψ m represents the position of the mth user; σ 2is the noise power; Γ n represents the signal to interference plus noise ratio of the nth perceived target; ψ pin represents the position vector of the pinching antenna; p represents the transmission power vector; M represents the total number of users; λ sen The signal-to-interference-plus-noise ratio threshold representing the perceived information.

[0037] Further, the multi-objective optimization algorithm is a genetic algorithm, a particle swarm optimization algorithm or a reinforcement learning algorithm;

[0038] The dynamic adjustment module is specifically used for:

[0039] According to the generated optimal resource allocation plan, the position, spectrum allocation and power allocation of the Pinching antenna are dynamically adjusted to achieve resource allocation. The resource allocation process is continuously optimized using the reinforcement learning algorithm, so that the collaborative performance of communication and perception functions is always in the optimal state.

[0040] When the user moves or the link quality fluctuates, the resource allocation plan is re-optimized and adjusted in real time.

[0041] 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.

[0042] 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.

[0043] The beneficial effects brought about by the technical solution provided by the present invention include at least:

[0044] The solution of the present invention comprehensively considers the communication needs and perception needs in the integrated network assisted by the pinching antenna, analyzes the link characteristics of the pinching antenna and establishes a link model, builds a resource allocation model based on the collaborative optimization goals of communication performance and perception accuracy, and considers the dynamic link adjustment capability of the pinching antenna. The model adopts a multi-objective optimization method to solve the resource allocation scheme, thereby ensuring the optimal collaborative performance of the communication and perception functions. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] 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.

[0046] Figure 1 It is a schematic diagram of a synaesthesia integrated network model assisted by a pinching antenna provided in an embodiment of the present invention;

[0047] Figure 2 It is a schematic diagram of the execution flow of the Pinching antenna-assisted synaesthesia integrated network resource allocation method provided in an embodiment of the present invention;

[0048] Figure 3 It is a system block diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order 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.

[0050] 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.

[0051] First embodiment

[0052] Aiming at the synaesthesia integrated network scenario, this embodiment provides a synaesthesia integrated network resource allocation method assisted by a pinching antenna, and designs a network resource allocation method that uses pinching antennas to enhance existing line-of-sight links or establish new line-of-sight links. Based on the characteristics of the pinching antenna, the performance of existing line-of-sight transmission links in the synaesthesia integrated network is enhanced or new line-of-sight transmission links are constructed. The communication and perception coordination performance of the synaesthesia integrated network is improved. Among them, the synaesthesia integrated network model assisted by the pinching antenna is as follows: Figure 1 The execution flow of this method is as follows Figure 2 Specifically, the method comprises the following steps:

[0053] S1, analyze the link characteristics of the Pinching antenna and establish a link model to determine the communication performance requirements and perception accuracy requirements of the Pinching antenna-assisted synaesthesia integrated network;

[0054] Among them, the Pinching antenna uses its leakage wave characteristics to enhance or rebuild the wireless link, including enhancing the signal strength of an existing line-of-sight transmission link or establishing a new transmission link without line-of-sight.

[0055] This embodiment is based on the technical parameters of the Pinching antenna, including its leakage wave characteristics, transmission characteristics, etc. The link performance of the Pinching antenna under different transmission distances, positions and signal power conditions is evaluated in a simulation environment. A mathematical link model is established to describe the role of the Pinching antenna in line-of-sight transmission link enhancement or non-line-of-sight link reconstruction. The communication performance requirements and perception accuracy requirements of the interawareness integrated network are determined according to actual conditions.

[0056] S2, comprehensively considers the dynamic link adjustment capability of Pinching antenna, and establishes a resource allocation model based on the communication performance requirements and perception accuracy requirements of the interaceptive integrated network assisted by Pinching antenna;

[0057] Among them, the collaborative optimization objectives of the resource allocation model include the data rate of the communication user and the signal to interference and noise ratio performance of the perception target, requiring the data rate of the communication user to be maximized; and the signal to interference and noise ratio (SINR) of the perception target to be greater than the set threshold.

[0058] Specifically, this embodiment comprehensively considers the dynamic link adjustment capability of the Pinching antenna, incorporates the synergy of communication performance (such as data rate) and perception accuracy (such as SINR) into the optimization target, and thus constructs a resource allocation model, which covers the following variables: the position of the Pinching antenna and power allocation.

[0059] Assume that in time slot t, the positions of pinching antenna and user m are expressed as ψ m =(x m ,y m ), 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 the data rate of user m is: While optimizing the user data rate, the signal to interference plus noise ratio (SINR) of the sensing target n is considered. n is greater than a given threshold; therefore, the resource allocation model can be expressed as:

[0060]

[0061] stΓ n ≥λ sen

[0062] Among them, ψ pin represents the position vector of the pinching antenna; p represents the transmission power vector; M represents the total number of users; λ senThe signal-to-interference-plus-noise ratio threshold representing the perceived information.

[0063] S3, using a multi-objective optimization algorithm to solve the resource allocation model and generate the optimal resource allocation plan;

[0064] Among them, the multi-objective optimization method includes a swarm intelligence optimization method, such as a genetic algorithm, a particle swarm optimization algorithm, or an optimization method based on reinforcement learning. Specifically, the implementation process of the above S3 is as follows:

[0065] S31, select a suitable multi-objective optimization algorithm, such as a swarm intelligence genetic algorithm, a particle swarm optimization algorithm, or an optimization method based on reinforcement learning.

[0066] S32, initializing the resource allocation model, including setting initial variables and defining constraints.

[0067] S33, solving the resource allocation model using an optimization algorithm, and iteratively generating an optimal resource allocation solution that meets the optimization goal.

[0068] S34 verifies the effectiveness of the optimal solution to ensure that the communication data rate reaches the optimal value while satisfying the perception performance.

[0069] S4, based on the generated optimal resource allocation plan, dynamically adjusts the position, spectrum allocation, and power allocation of the Pinching antenna to ensure the optimal collaborative performance of communication and perception functions.

[0070] Specifically, the above S4 optimizes the resource allocation scheme through a reinforcement learning algorithm according to the channel state information of the real-time link and the pinching antenna position change; the implementation process is as follows:

[0071] S41, dynamically adjusting the position, spectrum allocation, and power allocation of the Pinching antenna according to the optimal resource allocation solution.

[0072] S42, using reinforcement learning algorithm to continuously optimize the resource allocation process so that the synergy performance of communication and perception functions is always in the optimal state.

[0073] S43, when the network environment changes, such as user location movement or link quality fluctuation, the resource allocation scheme is re-optimized and adjusted in real time.

[0074] In summary, this embodiment provides a Pinching antenna-assisted synaesthesia integrated network resource allocation method, which comprehensively considers the communication needs and perception needs in the Pinching antenna-assisted integrated network, analyzes the link characteristics of the pinching antenna and establishes a link model, and constructs a resource allocation model based on the collaborative optimization goals of communication performance and perception accuracy. The model considers the dynamic link adjustment capability of the pinching antenna, and uses a multi-objective optimization method to solve the resource allocation plan, thereby ensuring the optimal collaborative performance of communication and perception functions.

[0075] Second embodiment

[0076] This embodiment provides a Pinching antenna-assisted synaesthesia integrated network resource allocation device, and the Pinching antenna-assisted synaesthesia integrated network resource allocation device includes the following modules:

[0077] Data initialization module, used to analyze the link characteristics of the Pinching antenna and establish a link model, and determine the communication performance requirements and perception accuracy requirements of the interawareness integrated network assisted by the Pinching antenna;

[0078] The resource allocation model building module is used to comprehensively consider the dynamic link adjustment capability of the Pinching antenna and establish a resource allocation model based on the communication performance requirements and perception accuracy requirements of the interaceptive integrated network assisted by the Pinching antenna;

[0079] The model solving module is used to solve the resource allocation model using a preset multi-objective optimization algorithm to generate an optimal resource allocation solution;

[0080] The dynamic adjustment module is used to dynamically adjust the position, spectrum allocation and power allocation of the Pinching antenna according to the generated optimal resource allocation plan to ensure the optimal collaborative performance of communication and perception functions.

[0081] Among them, it should be noted that the Pinching antenna-assisted synaesthesia integrated network resource allocation device of this embodiment corresponds to the Pinching antenna-assisted synaesthesia integrated network resource allocation method of the above-mentioned first embodiment; wherein, the functions implemented by each functional module in the Pinching antenna-assisted synaesthesia integrated network resource allocation device of this embodiment correspond one-to-one to each process step in the Pinching antenna-assisted synaesthesia integrated network resource allocation method of the above-mentioned first embodiment; therefore, they will not be repeated here.

[0082] Third embodiment

[0083] This embodiment provides an electronic device, such as Figure 3As 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.

[0084] Next, combine Figure 3 The following is a detailed introduction to the various components of the electronic device:

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] Fourth embodiment

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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 A step that specifies a function in one or more boxes.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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-assisted synaesthesia integrated network resource allocation method, characterized in that: The Pinching antenna-assisted synaesthesia integrated network resource allocation method comprises: Analyze the link characteristics of the Pinching antenna and establish a link model to determine the communication performance requirements and perception accuracy requirements of the Pinching antenna-assisted synaesthesia integrated network; Taking into account the dynamic link adjustment capability of Pinching antennas, a resource allocation model is established based on the communication performance requirements and perception accuracy requirements of the interaceptive integrated network assisted by Pinching antennas. Use the preset multi-objective optimization algorithm to solve the resource allocation model and generate the optimal resource allocation plan; According to the generated optimal resource allocation plan, the position, spectrum allocation and power allocation of the Pinching antenna are dynamically adjusted to ensure the optimal collaborative performance of communication and perception functions.

2. The Pinching antenna-assisted synaesthesia integrated network resource allocation method according to claim 1, characterized in that: The analyzing the link characteristics of the Pinching antenna and establishing the link model includes: According to the parameters of the Pinching antenna, the link performance of the Pinching antenna under different transmission distances, positions and signal power conditions is evaluated in a simulation environment; a mathematical link model is established to describe the role of the Pinching antenna in enhancing the signal strength of an existing line-of-sight transmission link or establishing a new transmission link in the absence of line-of-sight.

3. The Pinching antenna-assisted synaesthesia integrated network resource allocation method according to claim 1, characterized in that: The optimization objectives of the resource allocation model include: maximizing the data rate of the communication users, and ensuring that the signal to interference noise ratio of the sensing target is greater than a set threshold.

4. The Pinching antenna-assisted synaesthesia integrated network resource allocation method according to claim 3, characterized in that: The resource allocation model is expressed as: st C n ≥λ sen in, k represents the spherical radiation parameter; p m represents the power allocated by the base station to the mth user; represents the position of the pinching antenna; ψ m represents the position of the mth user; σ 2 is the noise power; Γ n represents the signal to interference plus noise ratio of the nth perceived target; ψ pin represents the position vector of the pinching antenna; p represents the transmission power vector; M represents the total number of users; λ sen The signal-to-interference-plus-noise ratio threshold representing the perceived information.

5. The Pinching antenna-assisted synaesthesia integrated network resource allocation method according to claim 1, characterized in that: The multi-objective optimization algorithm is a genetic algorithm, a particle swarm optimization algorithm or a reinforcement learning algorithm.

6. The Pinching antenna-assisted synaesthesia integrated network resource allocation method according to claim 1, characterized in that: The method dynamically adjusts the position, spectrum allocation, and power allocation of the Pinching antenna according to the generated optimal resource allocation scheme to ensure optimal collaborative performance of communication and perception functions, including: According to the generated optimal resource allocation plan, the position, spectrum allocation and power allocation of the Pinching antenna are dynamically adjusted to achieve resource allocation. The resource allocation process is continuously optimized using the reinforcement learning algorithm, so that the collaborative performance of communication and perception functions is always in the optimal state. When the user moves or the link quality fluctuates, the resource allocation plan is re-optimized and adjusted in real time.

7. A Pinching antenna-assisted synaesthesia integrated network resource allocation device, characterized in that: The Pinching antenna-assisted synaesthesia integrated network resource allocation device comprises: Data initialization module, used to analyze the link characteristics of the Pinching antenna and establish a link model, and determine the communication performance requirements and perception accuracy requirements of the interawareness integrated network assisted by the Pinching antenna; The resource allocation model building module is used to comprehensively consider the dynamic link adjustment capability of the Pinching antenna and establish a resource allocation model based on the communication performance requirements and perception accuracy requirements of the interaceptive integrated network assisted by the Pinching antenna; The model solving module is used to solve the resource allocation model using a preset multi-objective optimization algorithm to generate an optimal resource allocation solution; The dynamic adjustment module is used to dynamically adjust the position, spectrum allocation and power allocation of the Pinching antenna according to the generated optimal resource allocation plan to ensure the optimal collaborative performance of communication and perception functions.

8. The Pinching antenna-assisted synaesthesia integrated network resource allocation device according to claim 7, characterized in that: The data initialization module is specifically used for: According to the parameters of the Pinching antenna, the link performance of the Pinching antenna under different transmission distances, positions and signal power conditions is evaluated in a simulation environment; a mathematical link model is established to describe the role of the Pinching antenna in enhancing the signal strength of an existing line-of-sight transmission link or establishing a new transmission link in the absence of line-of-sight.

9. The Pinching antenna-assisted synaesthesia integrated network resource allocation device according to claim 7, characterized in that: The optimization objectives of the resource allocation model include: maximizing the data rate of the communication user and ensuring that the signal to interference noise ratio of the sensing target is greater than a set threshold; The resource allocation model is expressed as: st C n ≥λ sen in, κ represents the spherical radiation parameter; p m represents the power allocated by the base station to the mth user; represents the position of the pinching antenna; ψ m represents the position of the mth user; σ 2 is the noise power; Γ n represents the signal to interference plus noise ratio of the nth perceived target; ψ pin represents the position vector of the pinching antenna; p represents the transmission power vector; M represents the total number of users; λ sen The signal-to-interference-plus-noise ratio threshold representing the perceived information.

10. The Pinching antenna-assisted synaesthesia integrated network resource allocation device according to claim 7, characterized in that: The multi-objective optimization algorithm is a genetic algorithm, a particle swarm optimization algorithm or a reinforcement learning algorithm; The dynamic adjustment module is specifically used for: According to the generated optimal resource allocation plan, the position, spectrum allocation and power allocation of the Pinching antenna are dynamically adjusted to achieve resource allocation. The resource allocation process is continuously optimized using the reinforcement learning algorithm, so that the collaborative performance of communication and perception functions is always in the optimal state. When the user moves or the link quality fluctuates, the resource allocation plan is re-optimized and adjusted in real time.

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