Power optimization method for multi-waveguide multi-pinching antenna assisted flux-inductance fusion NOMA (Non-Orthogonal Multiple Access) network

Abstract

The invention provides a method and a device for optimizing power of a multi-waveguide multi-pinching antenna-assisted non-orthogonal multiple access (NOMA) network, and relates to the technical field of wireless communication. The method comprises the following steps: based on a communication structure of a multi-waveguide multi-pinching antenna network, carrying out model construction according to communication fusion network information; based on transmission characteristics between waveguides, analyzing power distribution of users according to communication convergence network information to obtain power distribution parameters and transmission power distribution between the waveguides; constructing a first signal to interference plus noise ratio model and a second signal to interference plus noise ratio model; constructing a minimum power optimization target based on a preset communication constraint condition of the NOMA network; and based on the minimum power optimization target, according to the information of the communication convergence network to be optimized, performing power allocation by using a reinforcement learning method to obtain an optimal power allocation set. The method is an efficient and flexible method for optimizing the power of the non-orthogonal multiple access (NOMA) network aided by a multi-band multi-pinching antenna, and the method is used for optimizing the power of the non-orthogonal multiple access (NOMA) network.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and particularly to a power optimization method and device for an integrated sensing and communication non-orthogonal multiple access (ISAC-NOMA) network assisted by a multi-waveguide multi-pinching antenna. Background Art

[0002] Integrated Sensing and Communication (ISAC) technology integrates wireless communication and radar sensing functions onto the same platform, leveraging shared resources such as hardware, spectrum, and energy to improve operational efficiency, reduce costs, and promote sustainable development. The ISAC network realizes sensing functions such as identification, positioning, and imaging through wireless communication signals, and further enhances and explores potential communication capabilities using sensing information, enabling wireless signals to not only transmit effective communication information but also sense, detect, and characterize the physical world. In the research of ISAC, there have been numerous fusion studies on non-orthogonal multiple access (NOMA) technology. NOMA technology allows multiple users to share resources at the same time and on the same frequency through different power levels or coding methods. Through superposition coding and successive interference cancellation techniques, user signals are separated at the receiving end, enhancing system connectivity, reducing interference, and improving spectral efficiency.

[0003] The integrated sensing and communication non-orthogonal multiple access network (ISAC-NOMA) significantly expands the dimension of application scenarios by deeply embedding sensing capabilities into the wireless communication framework. However, its resource sharing mechanism and dynamic environment adaptability pose higher requirements for system design. The spectrum / hardware sharing of sensing and communication functions relies on a highly directional and reconfigurable antenna system to suppress mutual interference and improve resource reuse rate; complex time-varying channels require antennas to have real-time beam control capabilities to simultaneously ensure communication reliability and sensing accuracy.

[0004] Traditional fixed-architecture antennas are difficult to meet the above requirements due to limited degrees of freedom. Therefore, there is an urgent need to construct a new ISAC-NOMA network architecture based on a flexible antenna system, and through joint optimization of antenna position, resource allocation, and user scheduling, etc., to achieve dynamic coordination of sensing-communication functions.

[0005] In recent years, the pinching antenna in the flexible antenna system has received extensive attention. The pinching antenna system creates new line-of-sight links and / or enhances existing transceiver links by applying low-cost dielectric materials at any position on the dielectric waveguide. Different from traditional antennas, the pinching antenna can be flexibly deployed, and increasing its quantity hardly incurs additional costs. The pinching antenna circumvents the high costs of other flexible antennas and the problem of difficult confrontation with large-scale path loss. Its flexible radiation pattern and strong layout adaptability demonstrate broad application prospects. To fully exploit the potential of the pinching antenna, a multi-waveguide structure can be adopted, that is, each waveguide can carry a single or multiple pinching antennas, thus forming two architectures: the single-activated pinching antenna multi-waveguide architecture and the multi-activated pinching antenna multi-waveguide architecture. Through this pinching antenna design, the channel gain can be dynamically adjusted, thereby causing an adaptive change in the structure of the user channel space. This technical solution can significantly increase the possibility of the ideal upper limit value of the data rate. Currently, there is a lack of systematic research on the power optimization of multi-waveguide multi-pinching antennas in the communication-sensing integrated non-orthogonal access network.

[0006] In the existing technology, there is a lack of an efficient and flexible power optimization method for the communication-sensing integrated NOMA network assisted by multi-band multi-pinching antennas. Summary of the Invention

[0007] To solve the technical problem of the lack of systematic power optimization of multi-waveguide multi-pinching antennas in the communication-sensing integrated non-orthogonal access network existing in the prior art, an embodiment of the present invention provides a power optimization method and device for a communication-sensing integrated NOMA network assisted by multi-waveguide multi-pinching antennas. The technical solution is as follows:

[0008] On the one hand, a power optimization method for a communication-sensing integrated NOMA network assisted by multi-waveguide multi-pinching antennas is provided. This method is implemented by a power optimization device for the communication-sensing integrated NOMA network. The method includes:

[0009] Collect multi-waveguide multi-pinching antenna network data and obtain communication fusion network information;

[0010] Based on the communication structure of the multi-waveguide multi-pinching antenna network, construct a model according to the communication fusion network information to obtain a first channel model, a second channel model, and a transmission signal model;

[0011] Based on the transmission characteristics between waveguides, analyze the power allocation of users according to the communication fusion network information to obtain power allocation parameters and the transmission power allocation between waveguides;

[0012] Based on the transmission influence of the pinching antenna within the waveguide and the transmission influence of the pinching antenna between waveguides, according to the first channel model, the second channel model, the transmission signal model, the power allocation parameters, and the inter-waveguide transmission power allocation, the normalized signal-to-interference-plus-noise ratio is calculated to obtain the first signal-to-interference-and-noise ratio model and the second signal-to-interference-and-noise ratio model;

[0013] Based on the communication constraint conditions of the preset communication-sensing integrated NOMA network, a minimum power optimization objective is constructed according to the first signal-to-interference-and-noise ratio model and the second signal-to-interference-and-noise ratio model;

[0014] Obtain the information of the communication fusion network to be optimized; based on the minimum power optimization objective, according to the information of the communication fusion network to be optimized, use the reinforcement learning method for power allocation to obtain the optimal power allocation set.

[0015] On the other hand, a power optimization device for a communication-sensing integrated NOMA network assisted by multi-waveguide multi-pinching antennas is provided. The device is applied to the power optimization method of a communication-sensing integrated NOMA network assisted by multi-waveguide multi-pinching antennas. The device includes:

[0016] A network information acquisition module, configured to collect multi-waveguide multi-pinching antenna network data and obtain communication fusion network information;

[0017] A model construction module, configured to construct models based on the communication structure of the multi-waveguide multi-pinching antenna network and obtain the first channel model, the second channel model, and the transmission signal model according to the communication fusion network information;

[0018] A power analysis module, configured to analyze the power allocation of users based on the inter-waveguide transmission characteristics and obtain the power allocation parameters and the inter-waveguide transmission power allocation according to the communication fusion network information;

[0019] A signal-to-interference-and-noise ratio model construction module, configured to calculate the normalized signal-to-interference-plus-noise ratio based on the transmission influence of the pinching antenna within the waveguide and the transmission influence of the pinching antenna between waveguides, and obtain the first signal-to-interference-and-noise ratio model and the second signal-to-interference-and-noise ratio model according to the first channel model, the second channel model, the transmission signal model, the power allocation parameters, and the inter-waveguide transmission power allocation;

[0020] An optimization objective construction module, configured to construct a minimum power optimization objective based on the preset communication constraint conditions of the communication-sensing integrated NOMA network and according to the first signal-to-interference-and-noise ratio model and the second signal-to-interference-and-noise ratio model;

[0021] An optimal power allocation module is used to obtain information of the communication and sensing integrated network to be optimized; based on the minimum power optimization objective, according to the information of the communication and sensing integrated network to be optimized, a reinforcement learning method is used for power allocation to obtain an optimal power allocation set.

[0022] On the other hand, a power optimization device for a communication and sensing integrated NOMA network is provided. The power optimization device for the communication and sensing integrated NOMA network includes: a processor; a memory, on which computer-readable instructions are stored. When the computer-readable instructions are executed by the processor, any one of the methods in the above-mentioned power optimization method for a communication and sensing integrated NOMA network assisted by a multi-waveguide multi-pinching antenna is implemented.

[0023] On the other hand, a computer-readable storage medium is provided. At least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by a processor to implement any one of the methods in the above-mentioned power optimization method for a communication and sensing integrated NOMA network assisted by a multi-waveguide multi-pinching antenna.

[0024] The beneficial effects brought by the technical solutions provided in the embodiments of the present invention at least include:

[0025] The present invention proposes a power optimization method for a communication and sensing integrated NOMA network. Based on the architecture of a communication and sensing integrated non-orthogonal multiple access network assisted by a multi-waveguide multi-pinching antenna, by analyzing the power allocation parameters of each user in the waveguide and the power allocation between multi-waveguides, a normalized signal-to-interference-plus-noise ratio model for communication and sensing is constructed respectively. Considering constraints such as the minimum communication data rate, sensing threshold, and system energy, a reinforcement learning method is used to solve the power optimization problem. The present invention is an efficient and flexible power optimization method for a communication and sensing integrated NOMA network assisted by a multi-band multi-pinching antenna. Description of the Drawings

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0027] Figure 1 It is a flowchart of a power optimization method for a communication and sensing integrated NOMA network assisted by a multi-waveguide multi-pinching antenna provided by an embodiment of the present invention;

[0028] Figure 2 It is a schematic diagram of a communication and sensing integrated NOMA network model assisted by a multi-waveguide multi-pinching antenna provided by an embodiment of the present invention;

[0029] Figure 3 It is a block diagram of a power optimization device for a communication-sensing integrated NOMA network assisted by a multi-waveguide multi-pinching antenna provided by an embodiment of the present invention;

[0030] Figure 4 It is a schematic structural diagram of a power optimization device for a communication-sensing integrated NOMA network provided by an embodiment of the present invention. Specific embodiments

[0031] The technical solutions in the present invention will be described below with reference to the accompanying drawings.

[0032] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as an "example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, the use of the word "example" is intended to present concepts in a specific manner. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two.

[0033] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, their intended meanings are the same. "(of)", "corresponding", and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, their intended meanings are the same.

[0034] In the embodiments of the present invention, sometimes subscripts such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, their intended meanings are the same.

[0035] To make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0036] The embodiments of the present invention provide a power optimization method for a communication-sensing integrated NOMA network assisted by a multi-waveguide multi-pinching antenna. This method can be implemented by a power optimization device for a communication-sensing integrated NOMA network, and this power optimization device for a communication-sensing integrated NOMA network can be a terminal or a server. As Figure 1 shown in the flowchart of the power optimization method for a communication-sensing integrated NOMA network assisted by a multi-waveguide multi-pinching antenna, the processing flow of this method can include the following steps:

[0037] S1. Collect multi-waveguide multi-pinching antenna network data and obtain communication fusion network information.

[0038] Among them, the communication fusion network information includes the communication information from the antenna to the user, the communication information from the antenna to the sensing target, and the network parameters of non-orthogonal multiple access.

[0039] In a feasible implementation, as Figure 2 shown, the communication and sensing fusion NOMA network model assisted by a multi-waveguide multi-pinching antenna includes pinching antenna systems of waveguides, each waveguide is equipped with pinching antennas, and the pinching antennas can accommodate pinching mobile users by using non-orthogonal multiple access technology (Non-Orthogonal Multiple Access, NOMA). represents the position of the th antenna on the th waveguide, represents the user position served by the th antenna on the th waveguide. The number of antennas corresponds to the number of users. represents the transmit power of waveguide .

[0040] S2. A communication structure based on a multi-waveguide multi-pinching antenna network constructs a model according to the communication fusion network information to obtain a first channel model, a second channel model, and a transmission signal model.

[0041] Optionally, a communication structure based on a multi-waveguide multi-pinching antenna network constructs a model according to the communication fusion network information to obtain a first channel model, a second channel model, and a transmission signal model, including:

[0042] Construct a model of the communication channel between the multi-waveguide multi-pinching antenna and the user according to the communication fusion network information to obtain a first channel model;

[0043] Construct a model of the communication channel between the multi-waveguide multi-pinching antenna and the sensing target according to the communication fusion network information to obtain a second channel model;

[0044] Construct a model of the communication signal of the non-orthogonal multiple access network according to the communication fusion network information to obtain a transmission signal model.

[0045] In a feasible implementation, in the application scenario of multiple pinching antennas on multiple waveguides, the pinching antennas reconstruct a new line-of-sight transmission link with their leaky wave characteristics to establish a pinching antenna channel model; considering the non-orthogonal multiple access scenario, the signals on each waveguide are transmitted in a superimposed manner to establish a transmission signal model.

[0046] make For the The first waveguide The channel model of the user served by the antenna is expressed as the following formula (1):

[0047] (1):

[0048] in, represents the spherical wave parameters, is the natural base, represents the imaginary unit, is an irrational number, represents the wavelength, represents the norm operation, To superimpose the signal through the antenna The phase shift of , and for The location of the waveguide feeding point and the waveguide wavelength.

[0049] The channel models of the communication end and the perception end are constructed based on the communication fusion network information.

[0050] No. The transmission signal model of a waveguide is expressed as the following formula (2):

[0051] (2);

[0052] in, is the power allocation parameter and , Represents communication signals.

[0053] S3. Based on the transmission characteristics between waveguides and the communication fusion network information, the user's power allocation is analyzed to obtain power allocation parameters and the transmission power allocation between waveguides.

[0054] In a feasible implementation, by analyzing the power allocation parameters of each user in the waveguide , the transmission power between waveguides , construct a set of decision variables.

[0055] against A pinching antenna system with waveguides, each equipped with In the scenario of a pinching antenna, the user power allocation parameters in the waveguide , For waveguide The power allocation parameter set is Indicates waveguide The power allocation parameters of each user within.

[0056] For pinching antenna systems of waveguides, the transmitted power between waveguides .

[0057] S4. Based on the transmission impact of the pinching antenna within the waveguide and the transmission impact of the pinching antenna between waveguides, according to the first channel model, the second channel model, the transmission signal model, the power allocation parameters, and the transmitted power allocation between waveguides, perform normalized signal-to-interference-plus-noise ratio calculation to obtain the first signal-to-interference-plus-noise ratio model and the second signal-to-interference-plus-noise ratio model.

[0058] Optionally, based on the transmission impact of the pinching antenna within the waveguide and the transmission impact of the pinching antenna between waveguides, according to the first channel model, the second channel model, the transmission signal model, the power allocation parameters, and the transmitted power allocation between waveguides, perform normalized signal-to-interference-plus-noise ratio calculation to obtain the first signal-to-interference-plus-noise ratio model and the second signal-to-interference-plus-noise ratio model, including:

[0059] Based on the transmission impact of the pinching antenna within the waveguide and the transmission impact of the pinching antenna between waveguides, according to the first channel model and the second channel model, perform data analysis on the channel transmission state of the waveguide to obtain the first channel condition and the second channel condition;

[0060] Based on the communication process between the multi-waveguide multi-pinching antenna network and the user, according to the first channel model, the transmission signal model, the first channel condition, the power allocation parameters, and the transmitted power allocation between waveguides, perform normalized signal-to-interference-plus-noise ratio calculation to obtain the first signal-to-interference-plus-noise ratio model of the communication;

[0061] Based on the communication process between the multi-waveguide multi-pinching antenna network and the sensing target, according to the second channel model, the transmission signal model, the second channel condition, the power allocation parameters, and the transmitted power allocation between waveguides, perform normalized signal-to-interference-plus-noise ratio calculation to obtain the second signal-to-interference-plus-noise ratio model of the sensing target.

[0062] In a feasible implementation, according to the channel models of communication and sensing, use signal processing theory and mathematical formulas to calculate the normalized signal-to-interference-plus-noise ratio (Normalized Signal-to-Interference-plus-Noise Ratio, nSINR) for communication and sensing. By analyzing the transmission path, interference sources, and noise characteristics of the signal, determine the model expression of nSINR.

[0063] Let represent the antenna of waveguide ​ The user associated with the waveguide has a channel condition as shown in Equation (3) below

[0064] (3);

[0065] where and are the position of the waveguide feeding power source point and the waveguide wavelength.

[0066] For the waveguide serving the user , the normalized signal-to-interference-plus-noise ratio of communication is expressed as Equation (4) below:

[0067] (4);

[0068] where represents additive white Gaussian noise, , and .

[0069] S5. Based on the communication constraint conditions of the preset communication-sensing integrated NOMA network, construct a minimum power optimization objective according to the first signal-to-interference-plus-noise ratio model and the second signal-to-interference-plus-noise ratio model.

[0070] Optionally, based on the communication constraint conditions of the preset communication-sensing integrated NOMA network, constructing a minimum power optimization objective according to the first signal-to-interference-plus-noise ratio model and the second signal-to-interference-plus-noise ratio model includes:

[0071] Based on Shannon's theorem, calculate the data rate according to the first signal-to-interference-plus-noise ratio model and the second signal-to-interference-plus-noise ratio model to obtain the communication data rate constraint and the sensing data rate;

[0072] Based on the communication constraint conditions of the preset communication-sensing integrated NOMA network, construct a minimum power optimization objective according to the communication user data rate and the sensing data rate.

[0073] In a feasible implementation, the nSINR of the communication users served by the waveguide is sorted in ascending order. The nSINR of user can be expressed as , and according to the decoding rule of NOMA, the communication user data rate is Equation (5) below:

[0074] (5);

[0075] Similarly, for the sensing target the normalized signal-to-interference-plus-noise ratio , set the threshold of the sensing accuracy , the perception needs to satisfy .

[0076] In summary, based on the communication constraint conditions of the preset cross-sensory fusion NOMA network, the optimization objective can be expressed as the following formula (6):

[0077] (6);

[0078] Among them, represents the in-waveguide power distribution parameter vector, represents the inter-waveguide power distribution vector, is the duration within each time slot. To simplify the formula description and without causing ambiguity, the present invention omits the time index , for example, the time index omitted here in .

[0079] S6. Obtain the information of the communication fusion network to be optimized; based on the minimum power optimization objective, according to the information of the communication fusion network to be optimized, use the reinforcement learning method to perform power distribution and obtain the optimal power distribution set.

[0080] Among them, the reinforcement learning method includes the deep deterministic policy gradient algorithm for offline processing or the proximal policy optimization algorithm for online processing.

[0081] In a feasible implementation manner, an intelligent algorithm is used to solve the network optimization problem and obtain the optimal power distribution set.

[0082] The intelligent method can be an offline reinforcement learning method (such as the deep deterministic policy gradient algorithm, etc.), an online reinforcement learning method (such as the proximal policy optimization algorithm). Such methods have good adaptability and flexibility when dealing with complex non-convex optimization problems.

[0083] Based on the optimization problem model and constraint conditions, construct the action space, state space, and reward function of the reinforcement learning algorithm. The action space includes the in-waveguide power distribution parameter vector and the inter-waveguide power distribution vector ; the state space includes the positions of the antennas, the positions of the users, the positions of the sensing targets, and the system energy, etc.; the reward function can be set to minimize the system energy, that is . Subsequently, the reinforcement learning algorithm (such as the deep deterministic policy gradient algorithm, proximal policy optimization algorithm) trains the network according to its own update rules, and the trained reinforcement learning network can output the optimal power distribution set.

[0084] The present invention solves the optimization problem of the cross-sensory fusion non-orthogonal multiple access network, completes the power distribution of the flexible antenna system-assisted network, and is applicable to various cross-sensory fusion non-orthogonal multiple access scenarios.

[0085] The present invention proposes a power optimization method for a NOMA network through fusion, based on a communication-sensing integrated non-orthogonal multiple access network architecture assisted by multi-waveguide multi-pinching antennas. By analyzing the power distribution parameters of each user in the waveguide and the power distribution among multi-waveguides, normalized signal-to-interference-plus-noise ratio models for communication and sensing are respectively constructed. Considering constraints such as the minimum communication data rate, sensing threshold, and system energy, a reinforcement learning method is used to solve the power optimization problem. The present invention is an efficient and flexible power optimization method for a communication-sensing integrated NOMA network assisted by multi-band multi-pinching antennas.

[0086] Figure 3 It is a block diagram of a power optimization device for a communication-sensing integrated NOMA network assisted by multi-waveguide multi-pinching antennas shown according to an exemplary embodiment. This device is used for the power optimization method of a communication-sensing integrated NOMA network assisted by multi-waveguide multi-pinching antennas. Referring to Figure 3 , the device includes a network information acquisition module 310, a model construction module 320, a power analysis module 330, a signal-to-interference-plus-noise ratio model construction module 340, an optimization objective construction module 350, and an optimal power allocation module 360. Among them:

[0087] The network information acquisition module 310 is used to collect multi-waveguide multi-pinching antenna network data and obtain communication fusion network information;

[0088] The model construction module 320 is used to construct models based on the communication structure of the multi-waveguide multi-pinching antenna network and obtain a first channel model, a second channel model, and a transmission signal model according to the communication fusion network information;

[0089] The power analysis module 330 is used to analyze the power distribution of users based on the transmission characteristics between waveguides and obtain power distribution parameters and the transmission power distribution between waveguides according to the communication fusion network information;

[0090] The signal-to-interference-plus-noise ratio model construction module 340 is used to perform normalized signal-to-interference-plus-noise ratio calculations based on the pinching antenna transmission influence within the waveguide and the pinching antenna transmission influence between waveguides, and obtain a first signal-to-interference-plus-noise ratio model and a second signal-to-interference-plus-noise ratio model according to the first channel model, the second channel model, the transmission signal model, the power distribution parameters, and the transmission power distribution between waveguides;

[0091] The optimization objective construction module 350 is used to construct a minimum power optimization objective based on the preset communication constraint conditions of the communication-sensing integrated NOMA network according to the first signal-to-interference-plus-noise ratio model and the second signal-to-interference-plus-noise ratio model;

[0092] The optimal power allocation module 360 is configured to obtain information of the communication fusion network to be optimized; based on the minimum power optimization objective, according to the information of the communication fusion network to be optimized, use the reinforcement learning method to perform power allocation, and obtain an optimal power allocation set.

[0093] Among them, the communication fusion network information includes communication information from the antenna to the user, communication information from the antenna to the sensing target, and network parameters of non-orthogonal multiple access.

[0094] Optionally, the model construction module 320 is further configured to:

[0095] According to the communication fusion network information, construct a model of the communication channel between the multi-waveguide multi-pinching antenna and the user to obtain a first channel model;

[0096] According to the communication fusion network information, construct a model of the communication channel between the multi-waveguide multi-pinching antenna and the sensing target to obtain a second channel model;

[0097] According to the communication fusion network information, construct a model of the communication signal of the non-orthogonal multiple access network to obtain a transmission signal model.

[0098] Optionally, the signal-to-interference-plus-noise ratio model construction module 340 is further configured to:

[0099] Based on the influence of the pinching antenna transmission in the waveguide and the influence of the pinching antenna transmission between waveguides, according to the first channel model and the second channel model, perform data analysis on the channel transmission state of the waveguide to obtain a first channel condition and a second channel condition;

[0100] Based on the communication process between the multi-waveguide multi-pinching antenna network and the user, according to the first channel model, the transmission signal model, the first channel condition, the power allocation parameter, and the inter-waveguide transmission power allocation, perform normalized signal-to-interference-plus-noise ratio calculation to obtain a first signal-to-interference-plus-noise ratio model of communication;

[0101] Based on the communication process between the multi-waveguide multi-pinching antenna network and the sensing target, according to the second channel model, the transmission signal model, the second channel condition, the power allocation parameter, and the inter-waveguide transmission power allocation, perform normalized signal-to-interference-plus-noise ratio calculation to obtain a second signal-to-interference-plus-noise ratio model of the sensing target.

[0102] Optionally, the optimization objective construction module 350 is further configured to:

[0103] Based on Shannon's theorem, perform data rate calculation according to the first signal-to-interference-plus-noise ratio model and the second signal-to-interference-plus-noise ratio model to obtain a communication data rate constraint and a sensing data rate;

[0104] Based on the communication constraint conditions of the preset cross-sensory fusion NOMA network, a minimum power optimization objective is constructed according to the communication user data rate and the sensing data rate.

[0105] Among them, the reinforcement learning method includes the deep deterministic policy gradient algorithm for offline processing or the proximal policy optimization algorithm for online processing.

[0106] The present invention proposes a power optimization method for a cross-sensory fusion NOMA network. Based on the cross-sensory fusion non-orthogonal multiple access network architecture assisted by multi-waveguide multi-pinching antennas, by analyzing the power distribution parameters of each user in the waveguide and the power distribution between multi-waveguides, normalized signal-to-interference-plus-noise ratio models for communication and sensing are respectively constructed. Considering constraints such as the minimum communication data rate, sensing threshold, and system energy, a reinforcement learning method is used to solve the power optimization problem. The present invention is an efficient and flexible power optimization method for a cross-sensory fusion NOMA network assisted by multi-band multi-pinching antennas.

[0107] Figure 4 It is a schematic structural diagram of a cross-sensory fusion NOMA network power optimization device provided by an embodiment of the present invention. As Figure 4 shown, the cross-sensory fusion NOMA network power optimization device may include the above-mentioned Figure 3 cross-sensory fusion NOMA network power optimization device assisted by multi-waveguide multi-pinching antennas. Optionally, the cross-sensory fusion NOMA network power optimization device 410 may include a first processor 2001.

[0108] Optionally, the cross-sensory fusion NOMA network power optimization device 410 may further include a memory 2002 and a transceiver 2003.

[0109] Among them, the first processor 2001, the memory 2002, and the transceiver 2003 may be connected through a communication bus.

[0110] Next, in combination with Figure 4 each component of the cross-sensory fusion NOMA network power optimization device 410 will be specifically introduced:

[0111] Among them, the first processor 2001 is the control center of the communication-sensing integrated NOMA network power optimization device 410, which can be a single processor or a collective term for multiple processing elements. For example, the first processor 2001 is one or more central processing units (CPUs), or can be an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention, such as: one or more digital signal processors (DSPs), or one or more field programmable gate arrays (FPGAs).

[0112] Optionally, the first processor 2001 can execute various functions of the communication-sensing integrated NOMA network power optimization device 410 by running or executing software programs stored in the memory 2002 and calling data stored in the memory 2002.

[0113] In a specific implementation, as an embodiment, the first processor 2001 may include one or more CPUs, such as Figure 4 the CPU0 and CPU1 shown in

[0114] In a specific implementation, as an embodiment, the communication-sensing integrated NOMA network power optimization device 410 may also include multiple processors, such as Figure 4 the first processor 2001 and the second processor 2004 shown in

[0115] Each of these processors can be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). Here, the processor can refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions).

[0116] Optionally, the memory 2002 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 may also be 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 discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, 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 2002 may be integrated with the first processor 2001 or may exist independently and be coupled to the first processor 2001 through an interface circuit ( Figure 4 not shown) of the communication-sensing integrated NOMA network power optimization device 410. The embodiments of the present invention do not make specific limitations thereto.

[0117] The transceiver 2003 is configured to communicate with a network device or communicate with a terminal device.

[0118] Optionally, the transceiver 2003 may include a receiver and a transmitter ( Figure 4 not shown separately). Among them, the receiver is used to implement the receiving function, and the transmitter is used to implement the transmitting function.

[0119] Optionally, the transceiver 2003 may be integrated with the first processor 2001 or may exist independently and be coupled to the first processor 2001 through an interface circuit ( Figure 4 not shown) of the communication-sensing integrated NOMA network power optimization device 410. The embodiments of the present invention do not make specific limitations thereto.

[0120] It should be noted that Figure 4 the structure of the communication-sensing integrated NOMA network power optimization device 410 shown does not constitute a limitation to the router. The actual knowledge structure recognition device may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0121] In addition, the technical effects of the communication-sensing integrated NOMA network power optimization device 410 may refer to the technical effects of the communication-sensing integrated NOMA network power optimization method assisted by a multi-waveguide multi-pinching antenna described in the above method embodiments, and will not be elaborated here.

[0122] It should be understood that the first processor 2001 in the embodiments of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0123] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of random access memory (RAM) are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchlink dynamic random access memory (SLDRAM), and direct rambus random access memory (DR RAM).

[0124] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. 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 processes or functions described in the embodiments of the present invention are 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. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired (such as infrared, wireless, microwave, etc.) means. 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 that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, or magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0125] It should be understood that the term "and / or" in this document is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this document generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be specifically understood by referring to the context.

[0126] In the present invention, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following" or its similar expressions refer 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 represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0127] It should be understood that in various embodiments of the present invention, the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0128] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0129] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the devices, apparatuses, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again.

[0130] In several embodiments provided by the present invention, 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 the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0131] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0132] In addition, the functional units in each embodiment of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0133] When the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs.

[0134] As described above, the above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A power optimization method for a communication-sensing integrated NOMA network assisted by a multi-waveguide multi-pinching antenna, characterized in that, The method includes: Collecting multi-waveguide multi-pinching antenna network data to obtain communication fusion network information; Based on the communication structure of the multi-waveguide multi-pinching antenna network, constructing models according to the communication fusion network information to obtain a first channel model, a second channel model, and a transmission signal model; Based on the transmission characteristics between waveguides, analyzing the power allocation of users according to the communication fusion network information to obtain power allocation parameters and the transmission power allocation between waveguides; Based on the transmission influence of the pinching antenna within the waveguide and the transmission influence of the pinching antenna between waveguides, calculating the normalized signal-to-interference-plus-noise ratio according to the first channel model, the second channel model, the transmission signal model, the power allocation parameters, and the transmission power allocation between waveguides to obtain a first signal-to-interference-and-noise ratio model and a second signal-to-interference-and-noise ratio model; Based on the preset communication constraint conditions of the communication-sensing fusion NOMA network, constructing a minimum power optimization objective according to the first signal-to-interference-and-noise ratio model and the second signal-to-interference-and-noise ratio model; Obtaining the communication fusion network information to be optimized; based on the minimum power optimization objective, performing power allocation using the reinforcement learning method according to the communication fusion network information to be optimized to obtain an optimal power allocation set.

2. The power optimization method for the communication-sensing integrated NOMA network assisted by the multi-waveguide multi-pinching antenna according to claim 1, wherein, The communication fusion network information includes the communication information from the antenna to the user, the communication information from the antenna to the sensing target, and the network parameters of non-orthogonal multiple access.

3. The power optimization method for the communication-sensing integrated NOMA network assisted by the multi-waveguide multi-pinching antenna according to claim 1, wherein The constructing models according to the communication fusion network information based on the communication structure of the multi-waveguide multi-pinching antenna network to obtain a first channel model, a second channel model, and a transmission signal model includes: Constructing a model of the communication channel between the multi-waveguide multi-pinching antenna and the user according to the communication fusion network information to obtain a first channel model; Constructing a model of the communication channel between the multi-waveguide multi-pinching antenna and the sensing target according to the communication fusion network information to obtain a second channel model; Constructing a model of the communication signal of the non-orthogonal multiple access network according to the communication fusion network information to obtain a transmission signal model.

4. The power optimization method for the communication-sensing integrated NOMA network assisted by the multi-waveguide multi-pinching antenna according to claim 1, wherein, The calculating the normalized signal-to-interference-plus-noise ratio according to the first channel model, the second channel model, the transmission signal model, the power allocation parameters, and the transmission power allocation between waveguides based on the transmission influence of the pinching antenna within the waveguide and the transmission influence of the pinching antenna between waveguides to obtain a first signal-to-interference-and-noise ratio model and a second signal-to-interference-and-noise ratio model includes: Based on the transmission influence of the pinching antenna within the waveguide and the transmission influence of the pinching antenna between waveguides, performing data analysis on the channel transmission state of the waveguide according to the first channel model and the second channel model to obtain a first channel condition and a second channel condition; Based on the communication process between the multi-waveguide multi-pinching antenna network and the user, calculating the normalized signal-to-interference-plus-noise ratio according to the first channel model, the transmission signal model, the first channel condition, the power allocation parameters, and the transmission power allocation between waveguides to obtain a first signal-to-interference-and-noise ratio model of the communication; Based on the communication process between the multi-waveguide multi-pinching antenna network and the sensing target, the second signal-to-interference-plus-noise ratio (SINR) model of the sensing target is obtained by calculating the normalized signal-to-interference-plus-noise ratio according to the second channel model, the transmission signal model, the second channel condition, the power allocation parameters, and the inter-waveguide transmission power allocation.

5. The power optimization method for the communication-sensing integrated NOMA network assisted by the multi-waveguide multi-pinching antenna according to claim 1, wherein Based on the communication constraint conditions of the preset communication and sensing integrated non-orthogonal multiple access (NOMA) network, a minimum power optimization objective is constructed according to the first SINR model and the second SINR model, including: Based on Shannon's theorem, the data rate is calculated according to the first SINR model and the second SINR model to obtain the communication data rate constraint and the sensing data rate. Based on the communication constraint conditions of the preset communication and sensing integrated NOMA network, a minimum power optimization objective is constructed according to the communication user data rate and the sensing data rate.

6. The power optimization method for the communication-sensing integrated NOMA network assisted by the multi-waveguide multi-pinching antenna according to claim 1, wherein The reinforcement learning method includes the deep deterministic policy gradient algorithm for offline processing or the proximal policy optimization algorithm for online processing.

7. A power optimization device for a communication-sensing integrated NOMA network assisted by a multi-waveguide multi-pinching antenna, the power optimization device for the communication-sensing integrated NOMA network assisted by the multi-waveguide multi-pinching antenna is used to implement the power optimization method for the communication-sensing integrated NOMA network assisted by the multi-waveguide multi-pinching antenna according to any one of claims 1-6, characterized in that, The device includes: A network information acquisition module, configured to collect multi-waveguide multi-pinching antenna network data and obtain communication fusion network information. A model construction module, configured to construct a model based on the communication structure of the multi-waveguide multi-pinching antenna network and obtain the first channel model, the second channel model, and the transmission signal model according to the communication fusion network information. A power analysis module, configured to analyze the power allocation of users based on the transmission characteristics between waveguides and obtain the power allocation parameters and the inter-waveguide transmission power allocation according to the communication fusion network information. A SINR model construction module, configured to calculate the normalized signal-to-interference-plus-noise ratio based on the influence of pinching antenna transmission within the waveguide and the influence of pinching antenna transmission between waveguides, and obtain the first SINR model and the second SINR model according to the first channel model, the second channel model, the transmission signal model, the power allocation parameters, and the inter-waveguide transmission power allocation. An optimization objective construction module, configured to construct a minimum power optimization objective based on the communication constraint conditions of the preset communication and sensing integrated NOMA network according to the first SINR model and the second SINR model. An optimal power allocation module, configured to obtain the communication fusion network information to be optimized; based on the minimum power optimization objective, perform power allocation using the reinforcement learning method according to the communication fusion network information to be optimized, and obtain the optimal power allocation set.

8. The power optimization device for the communication-sensing integrated NOMA network assisted by the multi-waveguide multi-pinching antenna according to claim 7, wherein, The SINR model construction module is further configured to: Based on the influence of pinching antenna transmission within the waveguide and the influence of pinching antenna transmission between waveguides, perform data analysis on the channel transmission state of the waveguide according to the first channel model and the second channel model, and obtain the first channel condition and the second channel condition. Based on the communication process between the multi-waveguide multi-pinching antenna network and the user, calculate the normalized signal-to-interference-plus-noise ratio according to the first channel model, the transmission signal model, the first channel condition, the power allocation parameters, and the inter-waveguide transmission power allocation, and obtain the first SINR model of the communication. Based on the communication process between the multi-waveguide multi-pinching antenna network and the sensing target, the second signal-to-interference-plus-noise ratio (SINR) model of the sensing target is obtained by calculating the normalized signal-to-interference-plus-noise ratio according to the second channel model, the transmission signal model, the second channel condition, the power allocation parameter, and the inter-waveguide transmission power allocation.

9. A synaesthesia fusion NOMA network power optimization device, characterized in that, The communication and sensing fusion NOMA network power optimization device includes: A processor; A memory, on which computer-readable instructions are stored. When the computer-readable instructions are executed by the processor, the method described in any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium, characterized in that, Program code is stored in the computer-readable storage medium, and the program code can be called by the processor to execute the method described in any one of claims 1 to 6.

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