A Pinching antenna-assisted synaesthesia integrated network resource allocation method and device

Abstract

The present invention discloses a method and device for allocating resources in a synaesthesia-integrated network assisted by a Pinching antenna, belonging to the field of wireless communication technology. The method comprises: analyzing the link characteristics of the Pinching antenna and establishing a link model to determine the communication performance requirements and perception accuracy requirements of the synaesthesia-integrated network assisted by the Pinching antenna; comprehensively considering the dynamic link adjustment capabilities of the Pinching antenna and establishing a resource allocation model based on the communication performance requirements and perception accuracy requirements; using a multi-objective optimization algorithm to solve the resource allocation model and generate an optimal resource allocation plan; and dynamically adjusting the position, spectrum, and power allocation of the Pinching antenna based on the generated optimal resource allocation plan to ensure optimal synergistic performance of communication and perception functions. The solution of the present invention can ensure optimal synergistic performance of communication and perception functions in a synaesthesia-integrated network assisted by a Pinching antenna.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless communications, and in particular to a method and device for allocating network resources assisted by a pinching antenna and integrating synaesthesia. 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, Integrated Sensing and Communication (ISAC) has gradually become a research hotspot. By utilizing the same network resources to achieve efficient communication and environmental perception, ISAC technology can significantly improve spectrum efficiency, reduce system complexity, and provide innovative solutions for scenarios such as the Internet of Vehicles, drone navigation, and smart homes. In an ISAC system, the coordinated optimization of communication and perception performance and resource allocation are key technologies. Traditional multi-antenna designs often require a trade-off between communication performance and perception performance, making it difficult to simultaneously meet the high efficiency requirements of both. In addition, with the continuous increase in network users and perception tasks, how to dynamically and efficiently allocate resources with 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] Flexible antenna system configuration capability: The size of a pinching antenna system can be increased (or decreased) by simply adding additional pinchings (or freeing up 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] Addressing the resource allocation challenges of integrated synaesthesia networks, Pinching antennas, a novel antenna design technology, offer greater flexibility and low cost, offering greater freedom in optimizing synaesthesia network performance. However, existing research has yet to fully explore the potential of Pinching antennas in synaesthesia resource allocation. Therefore, a Pinching-assisted synaesthesia network resource allocation solution is urgently needed to achieve coordinated optimization of communication and perception performance, thereby meeting 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] In one aspect, the present invention provides a method for allocating network resources using a pinching antenna-assisted synaesthesia integration system. The method comprises:

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

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

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

[0013] Based on 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] Based on the parameters of the Pinching antenna, the link performance of the Pinching antenna under different transmission distances, locations, 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 existing line-of-sight transmission links or establishing new transmission links in non-line-of-sight situations.

[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 and 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; Indicates 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 transmit power vector; M represents the total number of users; λ sen 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 optimal collaborative performance of communication and perception functions, including:

[0023] Based on the generated optimal resource allocation plan, the position, spectrum allocation, and power allocation of the Pinching antenna are dynamically adjusted to achieve resource allocation. A reinforcement learning algorithm is then used to continuously optimize the resource allocation process, ensuring that the collaborative performance of communication and perception functions is always optimal.

[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] The data initialization module is used to analyze the link characteristics of the Pinching antenna and establish a link model to determine the communication performance and perception accuracy requirements of the Pinching antenna-assisted synaesthesia integrated network.

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

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

[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 to:

[0031] Based on the parameters of the Pinching antenna, the link performance of the Pinching antenna under different transmission distances, locations, 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 existing line-of-sight transmission links or establishing new transmission links in non-line-of-sight situations.

[0032] 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 and 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; Indicates 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 transmit power vector; M represents the total number of users; λ sen Signal-to-interference-plus-noise ratio threshold representing the perceived information.

[0037] Furthermore, 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] Based on the generated optimal resource allocation plan, the position, spectrum allocation, and power allocation of the Pinching antenna are dynamically adjusted to achieve resource allocation. A reinforcement learning algorithm is then used to continuously optimize the resource allocation process, ensuring that the collaborative performance of communication and perception functions is always optimal.

[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 the storage medium stores at least one instruction, 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 and perception requirements in an integrated network assisted by pinching antennas, analyzes the link characteristics of pinching antennas and establishes a link model. Based on the collaborative optimization goals of communication performance and perception accuracy, a resource allocation model is constructed. The model considers the dynamic link adjustment capability of pinching antennas and adopts a multi-objective optimization method to solve the resource allocation scheme, ensuring the optimal collaborative performance of 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 Schematic diagram of a pinching antenna-assisted synaesthesia integrated network model provided by an embodiment of the present invention;

[0047] Figure 2 1 is a schematic diagram of an execution flow of a Pinching antenna-assisted synaesthesia-integrated network resource allocation method provided in an embodiment of the present invention;

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

[0049] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0050] First, 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 an "example" 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 concepts in a concrete manner. In addition, in the embodiments of the present invention, the meaning of "and / or" can be both or 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 includes the following steps:

[0053] S1: Analyze the link characteristics of the Pinching antenna and establish a link model to determine the communication performance and perception accuracy requirements of the Pinching antenna-assisted interawareness 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 in the absence of line-of-sight.

[0055] This example uses the technical parameters of the Pinching antenna, including its leakage and transmission characteristics, to evaluate its link performance under different transmission distances, locations, and signal power conditions in a simulation environment. A mathematical link model is established to describe the role of the Pinching antenna in enhancing line-of-sight transmission links or reconstructing non-line-of-sight links. The communication performance and perception accuracy requirements of the integrated interawareness network are determined based on actual conditions.

[0056] S2: Considering the dynamic link adjustment capability of the Pinching antenna, a resource allocation model is established based on the communication performance and perception accuracy requirements of the Pinching antenna-assisted interawareness integrated network.

[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 that covers the following variables: the position and power allocation of the Pinching antenna.

[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) Γ of the perception 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 transmit power vector; M represents the total number of users; λ senSignal-to-interference-plus-noise ratio threshold representing the perceived information.

[0063] S3, uses 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 swarm intelligence optimization method, such as genetic algorithm, particle swarm optimization algorithm or 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, using an optimization algorithm to solve the resource allocation model, and iteratively generate 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 meeting 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, S4 above optimizes the resource allocation scheme through a reinforcement learning algorithm based on the channel state information of the real-time link and the change in the pinching antenna position. 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 uses reinforcement learning algorithms to continuously optimize the resource allocation process so that the collaborative 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 plan is re-optimized and adjusted in real time.

[0074] In summary, this embodiment provides a resource allocation method for a pinching antenna-assisted synaesthesia integrated network, which comprehensively considers the communication and perception requirements 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 adopts a multi-objective optimization method to solve the resource allocation plan, thereby ensuring the optimal collaborative performance of the communication and perception functions.

[0075] Second embodiment

[0076] This embodiment provides a device for allocating network resources using a pinching antenna and integrated synaesthesia. The device includes the following modules:

[0077] The data initialization module is used to analyze the link characteristics of the Pinching antenna and establish a link model to determine the communication performance and perception accuracy requirements of the Pinching antenna-assisted synaesthesia integrated network.

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

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

[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, which is loaded and executed by the processor to implement the method of the first embodiment described above. 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 A detailed introduction to the various components of the electronic device is given below:

[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 can 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 (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor can perform 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 FIG 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 and will not be repeated here.

[0088] Optionally, the memory may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device 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 compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store 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. The actual device may include more or fewer components than shown, or may 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 can refer to the technical effects described in the first embodiment above, and therefore will not be repeated here.

[0091] Fourth embodiment

[0092] This embodiment provides a computer-readable storage medium storing at least one instruction, which is loaded and executed by a processor to implement the method of the first embodiment described above. The computer-readable storage medium may be a ROM, random access memory, CD-ROM, magnetic tape, floppy disk, or optical data storage device. The instructions stored therein can be loaded by a processor in a terminal to execute the method described above.

[0093] Furthermore, it should be noted that the present invention may be provided as a method, apparatus, or computer program product. Therefore, embodiments of the present invention may take the form of a fully or partially hardware embodiment, a fully or partially software embodiment, or an embodiment combining software and hardware aspects. Furthermore, when implemented using software, embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present invention are fully or partially generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired connection (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. 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 drive.

[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 process 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 that can direct 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, thereby providing instructions 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 document, relational terms such as first and second are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any actual relationship or order between these entities or operations. The terms "include," "comprises," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or terminal device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal device. Without further limitation, an element defined by the phrase "comprising a..." does not preclude the presence of other identical elements in the process, method, article, or terminal device comprising the element. In addition, the term "and / or" is merely a description of an associative relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: the presence of A alone, the presence of A and B simultaneously, or the presence of B alone, where 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 "more" means two or more. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items 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 multiple.

[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. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0099] In the several embodiments provided herein, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of functional modules / units is merely a logical functional division. In actual implementation, other division methods may be used, such as multiple units or components being combined or integrated into another device, or some features being ignored or not implemented. Furthermore, the coupling or direct coupling or communication connection shown or discussed between each other may be through some interface, indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms. Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, i.e., they may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of the present embodiment according to actual needs. In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single 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 and includes several instructions for enabling 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 description is merely a preferred embodiment of the present invention. It should be noted that, although preferred embodiments of the present invention have been described, those skilled in the art, once understanding the basic inventive concepts of the present invention, may make various improvements and modifications without departing from the principles of the present invention. Such improvements and modifications should also be considered within the scope of protection of the present invention. Therefore, the appended claims are intended to be interpreted as covering the preferred embodiments and all variations 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 includes: Analyze the link characteristics of the Pinching antenna and establish a link model to determine the communication performance and perception accuracy requirements of the Pinching antenna-assisted synaesthesia integrated network; Taking into account the dynamic link adjustment capability of the Pinching antenna, a resource allocation model is established based on the communication performance requirements and perception accuracy requirements of the Pinching antenna-assisted synaesthesia integrated network. The resource allocation model is expressed as follows: stΓ n ≥λ sen in, κ represents the spherical radiation parameter; p m represents the power allocated by the base station to the mth user; Indicates 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 transmit power vector; M represents the total number of users; λ sen The signal-to-interference-plus-noise ratio threshold representing the perceived information; Use the preset multi-objective optimization algorithm to solve the resource allocation model and generate the optimal resource allocation plan; Based on 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: Based on the parameters of the Pinching antenna, the link performance of the Pinching antenna under different transmission distances, locations, 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 existing line-of-sight transmission links or establishing new transmission links in non-line-of-sight situations.

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 and noise ratio of the perception target is greater than a set threshold.

4. 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.

5. 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 based on the generated optimal resource allocation scheme to ensure optimal collaborative performance of communication and perception functions, including: Based on the generated optimal resource allocation plan, the position, spectrum allocation, and power allocation of the Pinching antenna are dynamically adjusted to achieve resource allocation. A reinforcement learning algorithm is then used to continuously optimize the resource allocation process, ensuring that the collaborative performance of communication and perception functions is always optimal. When the user moves or the link quality fluctuates, the resource allocation plan is re-optimized and adjusted in real time.

6. A Pinching antenna-assisted synaesthesia integrated network resource allocation device, characterized in that: The Pinching antenna-assisted synaesthesia integrated network resource allocation device includes: The data initialization module is used to analyze the link characteristics of the Pinching antenna and establish a link model to determine the communication performance and perception accuracy requirements of the Pinching antenna-assisted synaesthesia integrated network. The resource allocation model construction 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 Pinching antenna-assisted synaesthesia integrated network. The resource allocation model is expressed as: stΓ n ≥λ sen in, κ represents the spherical radiation parameter; p m represents the power allocated by the base station to the mth user; Indicates 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 transmit power vector; M represents the total number of users; λ sen The signal-to-interference-plus-noise ratio threshold representing the perceived information; The model solving module is used to solve the resource allocation model using a preset multi-objective optimization algorithm to generate the optimal resource allocation plan; 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.

7. The Pinching antenna-assisted synaesthesia integrated network resource allocation device according to claim 6, characterized in that: The data initialization module is specifically used for: Based on the parameters of the Pinching antenna, the link performance of the Pinching antenna under different transmission distances, locations, 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 existing line-of-sight transmission links or establishing new transmission links in non-line-of-sight situations.

8. The Pinching antenna-assisted synaesthesia integrated network resource allocation device according to claim 6, 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 and noise ratio of the perception target is greater than a set threshold.

9. The Pinching antenna-assisted synaesthesia integrated network resource allocation device according to claim 6, 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: Based on the generated optimal resource allocation plan, the position, spectrum allocation, and power allocation of the Pinching antenna are dynamically adjusted to achieve resource allocation. A reinforcement learning algorithm is then used to continuously optimize the resource allocation process, ensuring that the collaborative performance of communication and perception functions is always optimal. 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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