Flexible antenna-assisted communication and inductance fusion NOMA network resource scheduling method and device

Abstract

The invention provides a flexible antenna-assisted NOMA (Non-Orthogonal Multiple Access) network resource scheduling method and device, and the method comprises the steps: exploring a flexible antenna-assisted NOMA network architecture, considering a scene of a plurality of pinching antennas on the same waveguide, enabling a user to access a network in a non-orthogonal multiple access manner in a clustering manner, and comprehensively considering communication and sensing demands, the method comprises the following steps: reconstructing a sight distance transmission link according to leaky-wave characteristics of a pinching antenna, and establishing a pinching antenna link channel model and a transmission signal model; constructing a decision variable set; comprehensively analyzing the communication data rate of the user cluster and the detection signal power of the sensing target, introducing a regularization coefficient according to a channel model, a transmission signal model and a decision variable set, constructing a regularization clustering user data rate and sensing target detection power model, optimizing a target through the sensing performance, and solving by using an artificial intelligence algorithm, so as to obtain the sensing performance of the user cluster. And obtaining an optimal correlation and power distribution set. According to the invention, resource scheduling can be carried out on the communication and inductance fusion NOMA network.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and particularly to a method and apparatus for resource scheduling in an integrated sensing and communication non-orthogonal multiple access (ISAC-NOMA) network assisted by a flexible 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, the separation of user signals is achieved at the receiving end, enhancing system connectivity, reducing interference, and improving spectrum efficiency.

[0003] The integrated sensing and communication non-orthogonal multiple access network (ISAC-NOMA) endows wireless communication with stronger sensing capabilities, giving rise to richer application scenarios. The utilization efficiency of shared resources and the adaptability to dynamic environments pose more challenges to the ISAC non-orthogonal access system, especially its antenna system. On the one hand, the resource sharing between sensing and communication functions requires the use of highly directional and flexible antennas to reduce signal interference and improve resource utilization efficiency. On the other hand, in a complex and time-varying environment, the antenna must have the ability to dynamically adapt to environmental changes to ensure communication quality and sensing accuracy. However, traditional fixed-position antenna designs often struggle to meet requirements such as the utilization efficiency of shared resources and adaptability to dynamic environments. Therefore, it is necessary to construct an ISAC-NOMA network architecture oriented to a flexible antenna system.

[0004] In recent years, flexible antenna systems (such as pinching antennas) have received extensive attention. The pinching antenna system creates new line-of-sight links and / or enhances existing transceiver channels by applying low-cost dielectric materials at any position on the dielectric waveguide. Different from traditional antennas, pinching antennas can be flexibly deployed, and increasing their 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 show broad application prospects. However, there is currently a lack of systematic research on resource scheduling of pinching antennas in communication-sensing integrated non-orthogonal access networks. Summary of the Invention

[0005] To solve the above technical problems existing in the prior art, the present invention provides a flexible antenna-assisted communication-sensing integrated NOMA network resource scheduling method and device, and the technical solutions are as follows:

[0006] On the one hand, a flexible antenna-assisted communication-sensing integrated NOMA network resource scheduling method is provided, and the method includes:

[0007] S1. Explore a flexible antenna-assisted communication-sensing integrated non-orthogonal multiple access (NOMA) network architecture. The network architecture considers the scenario of multiple pinching antennas on the same waveguide. In this scenario, communication users access the network non-orthogonally in the form of clusters, and comprehensively consider communication and sensing requirements. According to the leakage wave characteristics of the pinching antenna, reconstruct the line-of-sight transmission link, and establish a pinching antenna link channel model and a transmission signal model;

[0008] S2. Construct a decision variable set, and the decision variable set includes: the correlation α between the flexible antenna and the user cluster, the power allocation parameter η of the users within the cluster, and the transmission power p of the communication user and the sensing target;

[0009] S3. Comprehensively analyze the communication data rate of the user cluster and the detection signal power of the sensing target. According to the pinching antenna link channel model and the transmission signal model, as well as the decision variable set, introduce a regularization coefficient, and construct a regularized clustered user data rate and sensing target detection power model, and a communication-sensing performance optimization target;

[0010] S4. Use an artificial intelligence algorithm to solve the communication-sensing performance optimization target. The communication-sensing performance optimization target is a complex non-convex optimization problem with continuous-discrete mixed variables, and obtain the optimal correlation and power allocation set.

[0011] Optionally, in S1, establishing the pinching antenna link channel model specifically includes:

[0012] Let the positions of antenna \(l\) and user \(i\) in the \(m\)-th cluster be respectively ψ m,i =(x m,i ,y m,i ,0), where \(d\) represents the height of the antenna. Each cluster of users contains strong user \(s\) and weak user \(w\). Based on the spherical wave channel model, the channel gain from antenna \(l\) to user \(U(m,i)\) is expressed as:

[0013]

[0014] where \(i\in\{s,w\}\), \(\tau\) represents the spherical wave parameter, \(e\) is the base of the natural logarithm, \(j\) represents the imaginary unit, \(\pi\) is an irrational number, \(\lambda\) represents the wavelength, and \(\|\cdot\|\) represents the norm operation.

[0015] Optionally, establishing the pinching antenna link transmission signal model in the said S1 specifically includes:

[0016] Due to multiple pinching antennas on the same waveguide, the signals of all users are transmitted in a superimposed manner. The signals of \(M\) clusters of users are expressed as:

[0017]

[0018] where \(M\) represents the total number of user clusters, and the intra-cluster power allocation parameter x m,i , \(i\in\{s,w\}\) represents the communication signal sent to \(U(m,i)\).

[0019] Optionally, the said S3 specifically includes:

[0020] According to the successive interference cancellation rule of non-orthogonal multiple access (NOMA), the data rates of weak user \(U(m,w)\) and strong user \(U(m,s)\) are respectively expressed as:

[0021]

[0022] where the correlation \(\alpha\) between flexible antenna \(l\) and user cluster \(m\) l,m \(\in\{0,1\}\), \(\alpha = (\alpha l,m ) l∈L,m∈M , \(p m is the allocated power of user cluster \(m\), the intra-cluster power allocation parameter h l,m,w , \(h l,m,s are respectively the channel gains of the weak user and the strong user, \(\theta l is the phase shift of the superimposed signal passing through antenna \(l\), and respectively represent the intra-cluster interference and inter-cluster interference of the weak user, represents the inter-cluster interference of the strong user, \(\sigma 2denotes additive white Gaussian noise;

[0023] Therefore, the data rate of user cluster m is expressed as R m = R m,w + R m,s ;

[0024] The detection signal power P for sensing the target direction k is determined by the covariance matrix M = xx of the transmitted signal vector H and the channel vector h from the sensing target k to the pinching antenna k and is expressed as

[0025] The optimization objective is expressed as maximizing the data rate of the clustered users and the detection signal power of the sensing target:

[0026]

[0027] where α represents the correlation vector between the antenna and the user cluster, which is a discrete variable, η represents the intra-cluster power allocation parameter vector, which is a continuous variable, p represents the transmission power vector, which is a continuous variable, M represents the total number of user clusters, K represents the total number of sensing targets, and γ c ≥ 0, γ s ≥ 0 represents the regularization coefficients for communication and sensing. By introducing the regularization coefficients, the complexity of the model is restricted and the generalization ability of the model is improved.

[0028] Optionally, the artificial intelligence algorithm in S4 includes: Deep Deterministic Policy Gradient algorithm, Reinforcement Learning algorithm.

[0029] On the other hand, a flexible antenna-assisted communication and sensing fusion NOMA network resource scheduling device is provided, and the device includes:

[0030] A building module, configured to explore a flexible antenna-assisted communication and sensing fusion non-orthogonal multiple access NOMA network architecture. The network architecture considers the scenario of multiple pinching antennas on the same waveguide. In this scenario, communication users access the network non-orthogonally in the form of clusters, and comprehensively consider communication and sensing requirements. According to the leaky wave characteristics of the pinching antenna, the line-of-sight transmission link is reconstructed, and the pinching antenna link channel model and the transmission signal model are established;

[0031] A first construction module, configured to construct a decision variable set, and the decision variable set includes: the correlation α between the flexible antenna and the user cluster, the intra-cluster user power allocation parameter η, and the transmission power p of the communication user and the sensing target;

[0032] The second construction module is used to comprehensively analyze the communication data rate of user clusters and the detection signal power of sensing targets. According to the pinching antenna link channel model, the transmission signal model, and the decision variable set, a regularization coefficient is introduced to construct a regularization cluster user data rate and sensing target detection power model, as well as a communication and sensing performance optimization objective.

[0033] The solution module is used to solve the communication and sensing performance optimization objective by using an artificial intelligence algorithm. The communication and sensing performance optimization objective is a complex non-convex optimization problem with continuous-discrete mixed variables, and an optimal correlation and power allocation set is obtained.

[0034] Optionally, the establishment module is specifically used for:

[0035] Let the positions of antenna l and user i in the m-th cluster be ψ m,i =(x m,i , y m,i , 0), where d represents the height of the antenna. Each cluster of users contains strong user s and weak user w. Based on the spherical wave channel model, the channel gain from antenna l to user U(m,i) is expressed as:

[0036]

[0037] where i∈{s,w}, τ represents the spherical wave parameter, e is the natural base, j represents the imaginary unit, π is an irrational number, λ represents the wavelength, and ||·|| represents the norm operation.

[0038] Optionally, the establishment module is specifically further used for:

[0039] Due to multiple pinching antennas on the same waveguide, the signals of all users are transmitted in a superimposed manner. The signals of M clusters of users are expressed as:

[0040]

[0041] where M represents the total number of user clusters, and the in-cluster power allocation parameter x m,i , i∈{s,w} represents the communication signal sent to U(m,i).

[0042] Optionally, the second construction module is specifically used for:

[0043] According to the successive interference cancellation rule of non-orthogonal multiple access (NOMA), the data rates of weak user U(m,w) and strong user U(m,s) are respectively expressed as:

[0044]

[0045] Among them, the correlation α between the flexible antenna l and the user cluster m l,m ∈{0,1}, α=(α l,m ) l∈L,m∈M , p m is the allocated power of the user cluster m, and the intra-cluster power allocation parameter h l,m,w , h l,m,s are the channel gains of the weak user and the strong user respectively, and θ l is the phase shift of the superimposed signal passing through the antenna l, and represent the intra-cluster interference and inter-cluster interference of the weak user respectively, represents the inter-cluster interference of the strong user, and σ 2 represents the additive white Gaussian noise;

[0046] Therefore, the data rate of the user cluster m is expressed as R m =R m,w +R m,s ;

[0047] The power P of the detection signal for sensing the target direction k is determined by the covariance matrix M = xx H of the transmit signal vector and the channel vector h k from the sensing target k to the pinching antenna, and is expressed as

[0048] The optimization objective is expressed as maximizing the data rate of the clustered users and the power of the detection signal of the sensing target:

[0049]

[0050] Among them, α represents the correlation vector between the antenna and the user cluster, which is a discrete variable, η represents the intra-cluster power allocation parameter vector, which is a continuous variable, p represents the transmission power vector, which is a continuous variable, M represents the total number of user clusters, K represents the total number of sensing targets, γ c ≥0, γ s ≥0 represents the regularization coefficients of communication and sensing. By introducing the regularization coefficients, the complexity of the model is restricted and the generalization ability of the model is improved.

[0051] Optionally, the artificial intelligence algorithms of the solving module include: Deep Deterministic Policy Gradient algorithm, Reinforcement Learning algorithm.

[0052] The beneficial effects brought by the technical solution provided by the present invention at least include:

[0053] The present invention solves the resource scheduling problem in the communication-sensing integrated non-orthogonal multiple access network, maximizes the data rate of clustered users and the detection power of sensing targets (that is, maximizes the communication performance and sensing performance), and is applicable to various communication-sensing integrated non-orthogonal multiple access scenarios. Description of the Drawings

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings required for the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description 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.

[0055] Figure 1 is a flowchart of a resource scheduling method for a communication-sensing integrated NOMA network assisted by a flexible antenna provided by an embodiment of the present invention;

[0056] Figure 2 is a schematic diagram of a communication-sensing integrated non-orthogonal multiple access (NOMA) network assisted by a flexible antenna provided by an embodiment of the present invention;

[0057] Figure 3 is a block diagram of a resource scheduling device for a communication-sensing integrated NOMA network assisted by a flexible antenna provided by an embodiment of the present invention. Detailed Embodiments

[0058] To make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0059] An embodiment of the present invention provides a resource scheduling method for a communication-sensing integrated NOMA network assisted by a flexible antenna. This method can be implemented by an electronic device, which can be a terminal or a server. Figure 1 As shown in the flowchart of the method, the processing flow may include the following steps:

[0060] S1. Explore the flexible antenna-assisted communication-sensing integrated non-orthogonal multiple access (NOMA) network architecture. The network architecture considers the scenario of multiple pinching antennas on the same waveguide. In this scenario, communication users access the network non-orthogonally in the form of clustering (user clustering ensures that users with different channel conditions (such as strong users and weak users) can all obtain services. Through power-domain multiplexing, fairness among users is achieved. Reasonable user clustering can reduce interference among users, and users within a cluster can effectively separate their respective signals through successive interference cancellation technology. Within a cluster, more refined power allocation can be performed according to the channel conditions of users, thereby optimizing system performance. User clustering provides the network with higher flexibility and scalability, enabling the network to dynamically adjust the size and number of clusters according to user distribution and service requirements), and comprehensively consider communication and sensing requirements (such as Figure 2 as shown), reconstruct the line-of-sight transmission link according to the leaky wave characteristics of the pinching antenna, and establish the pinching antenna link channel model and transmission signal model;

[0061] Optionally, establishing the pinching antenna link channel model in S1 specifically includes:

[0062] Let the positions of antenna l and user i in the m-th cluster be ψ m,i =(x m,i ,y m,i ,0), where d represents the height of the antenna. Each cluster of users includes a strong user s and a weak user w. Based on the spherical wave channel model, the channel gain from antenna l to user U(m,i) is expressed as:

[0063]

[0064] where i∈{s,w}, τ represents the spherical wave parameter, e is the natural base, j represents the imaginary unit, π is an irrational number, λ represents the wavelength, and ||·|| represents the norm operation.

[0065] Optionally, establishing the pinching antenna link transmission signal model in S1 specifically includes:

[0066] Due to multiple pinching antennas on the same waveguide, the signals of all users are transmitted in a superimposed manner. The signals of M clusters of users are expressed as:

[0067]

[0068] where M represents the total number of user clusters, and the in-cluster power allocation parameter x m,i , i∈{s,w} represents the communication signal sent to U(m,i).

[0069] S2. Construct a set of decision variables, which includes: the correlation α between the flexible antenna and the user cluster, the power allocation parameter η of the users within the cluster, and the transmission power p of the communication users and the sensing targets;

[0070] S3. Comprehensively analyze the communication data rate of the user cluster and the detection signal power of the sensing targets. According to the pinching antenna link channel model and the transmission signal model, as well as the set of decision variables, introduce a regularization coefficient to construct a regularized clustered user data rate and sensing target detection power model, and a communication and sensing performance optimization objective;

[0071] Optionally, step S3 specifically includes:

[0072] According to the successive interference cancellation rule of non - orthogonal multiple access (NOMA), the data rates of the weak user U(m, w) and the strong user U(m, s) are respectively expressed as:

[0073]

[0074] where the correlation α between the flexible antenna l and the user cluster m l,m ∈{0, 1}, α=(α l,m ) l∈L,m∈M , p m is the allocated power of the user cluster m, and the power allocation parameter within the cluster h l,m,w , h l,m,s are respectively the channel gains of the weak user and the strong user, θ l is the phase shift of the superimposed signal passing through the antenna l, and respectively represent the intra - cluster interference and the inter - cluster interference of the weak user, represents the inter - cluster interference of the strong user, and σ 2 represents the additive white Gaussian noise;

[0075] Therefore, the data rate of the user cluster m is expressed as R m =R m,w +R m,s ;

[0076] The detection signal power P k in the direction of the sensing target is determined by the covariance matrix M = xx H of the transmitted signal vector and the channel vector h k from the sensing target k to the pinching antenna, and is expressed as

[0077] The optimization objective is expressed as maximizing the clustered user data rate and the detection signal power of the sensing targets:

[0078]

[0079] Among them, α represents the correlation vector between the antenna and the user cluster, which is a discrete variable, η represents the intra-cluster power allocation parameter vector, which is a continuous variable, p represents the transmission power vector, which is a continuous variable, M represents the total number of user clusters, K represents the total number of sensing targets, and γ c ≥0, γ s ≥0 represents the regularization coefficient for communication and sensing. By introducing the regularization coefficient, the complexity of the model is restricted, and the generalization ability of the model is improved.

[0080] S4. Use an artificial intelligence algorithm to solve the above-mentioned communication and sensing performance optimization objective. The communication and sensing performance optimization objective is a complex non-convex optimization problem with continuous-discrete mixed variables, and obtain the optimal correlation and power allocation set.

[0081] Optionally, the artificial intelligence algorithm in S4 includes: Deep Deterministic Policy Gradient algorithm, Reinforcement Learning algorithm (such algorithms have good adaptability and flexibility in dealing with complex non-convex optimization problems with continuous-discrete mixed variables).

[0082] As Figure 3 shown, an embodiment of the present invention further provides a flexible antenna-assisted communication and sensing fusion NOMA network resource scheduling device, and the device includes:

[0083] A building module 310, configured to explore a flexible antenna-assisted communication and sensing fusion non-orthogonal multiple access (NOMA) network architecture. The network architecture considers the scenario of multiple pinching antennas on the same waveguide. In this scenario, communication users access the network non-orthogonally in the form of clustering, and comprehensively consider communication and sensing requirements. According to the leakage wave characteristics of the pinching antenna, the line-of-sight transmission link is reconstructed, and the pinching antenna link channel model and transmission signal model are established;

[0084] A first construction module 320, configured to construct a decision variable set, and the decision variable set includes: the correlation α between the flexible antenna and the user cluster, the intra-cluster user power allocation parameter η, and the transmission power p of the communication user and the sensing target;

[0085] A second construction module 330, configured to comprehensively analyze the communication data rate of the user cluster and the detection signal power of the sensing target. According to the pinching antenna link channel model and transmission signal model, and the decision variable set, introduce a regularization coefficient, and construct a regularization clustered user data rate and sensing target detection power model, and a communication and sensing performance optimization objective;

[0086] A solving module 340, configured to use an artificial intelligence algorithm to solve the communication and sensing performance optimization objective. The communication and sensing performance optimization objective is a complex non-convex optimization problem with continuous-discrete mixed variables, and obtain the optimal correlation and power allocation set.

[0087] Optionally, the establishing module is specifically configured to:

[0088] Let the positions of antenna l and user i in the m-th cluster be ψ m,i =(x m,i , y m,i , 0), where d represents the height of the antenna. Each cluster of users includes a strong user s and a weak user w. Based on the spherical wave channel model, the channel gain from antenna l to user U(m, i) is expressed as:

[0089]

[0090] where i ∈ {s, w}, τ represents the spherical wave parameter, e is the natural base, j represents the imaginary unit, π is an irrational number, λ represents the wavelength, and ||·|| represents the norm operation.

[0091] Optionally, the establishing module is further specifically configured to:

[0092] Due to multiple pinching antennas on the same waveguide, the signals of all users are transmitted in a superimposed manner. The signals of M clusters of users are expressed as:

[0093]

[0094] where M represents the total number of user clusters, and the in-cluster power allocation parameter x m,i , i ∈ {s, w} represents the communication signal sent to U(m, i).

[0095] Optionally, the second constructing module is specifically configured to:

[0096] According to the successive interference cancellation rule of non-orthogonal multiple access (NOMA), the data rates of the weak user U(m, w) and the strong user U(m, s) are respectively expressed as:

[0097]

[0098] where the correlation α l,m ∈ {0, 1}, α = (α l,m ), l∈L,m∈M , p m is the allocated power of user cluster m, and the in-cluster power allocation parameters h l,m,w , h l,m,s are the channel gains of the weak user and the strong user respectively, θ l is the phase shift of the superimposed signal passing through antenna l, and respectively represent the intra-cluster interference and inter-cluster interference of weak users, represents the inter-cluster interference of strong users, σ 2 represents additive white Gaussian noise;

[0099] Therefore, the data rate of user cluster m is expressed as R m = R m,w + R m,s ;

[0100] The power P of the detection signal for sensing the target direction k is determined by the covariance matrix M = xx of the transmitted signal vector H and the channel vector h from the sensing target k to the pinching antenna k and is expressed as

[0101] The optimization objective is expressed as maximizing the data rate of the clustered users and the power of the detection signal of the sensing target:

[0102]

[0103] where α represents the correlation vector between the antenna and the user cluster, which is a discrete variable, η represents the intra-cluster power allocation parameter vector, which is a continuous variable, p represents the transmission power vector, which is a continuous variable, M represents the total number of user clusters, K represents the total number of sensing targets, γ c ≥ 0, γ s ≥ 0 represents the regularization coefficients for communication and sensing. By introducing the regularization coefficients, the complexity of the model is restricted and the generalization ability of the model is improved.

[0104] Optionally, the artificial intelligence algorithms of the solving module include: deep deterministic policy gradient algorithm, reinforcement learning algorithm.

[0105] The function structure of a flexible antenna-assisted communication and sensing integrated NOMA network resource scheduling device provided by an embodiment of the present invention corresponds to a flexible antenna-assisted communication and sensing integrated NOMA network resource scheduling method provided by an embodiment of the present invention, and will not be elaborated here.

[0106] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A flexible antenna-assisted synaesthesia-fusion NOMA network resource scheduling method, characterized in that: The method comprises: S1. Explore the flexible antenna-assisted interaceptive fusion non-orthogonal multiple access NOMA network architecture. The network architecture considers the scenario of multiple pinching antennas on the same waveguide. In this scenario, communication users access the network in the form of clustered non-orthogonal multiple access. In addition, the communication and perception requirements are comprehensively considered. The line-of-sight transmission link is reconstructed according to the leakage wave characteristics of the pinching antenna, and the pinching antenna link channel model and transmission signal model are established. S2. Construct a decision variable set, wherein the decision variable set includes: a correlation α between the flexible antenna and the user cluster, a power allocation parameter η of the users in the cluster, and a transmission power p of the communication user and the sensing target; S3, comprehensively analyzing the user cluster communication data rate and the detection signal power of the perception target, introducing a regularization coefficient according to the pinching antenna link channel model and the transmission signal model, and the decision variable set, and constructing a regularized clustering user data rate and perception target detection power model, as well as a synaesthesia performance optimization target; S4. Using an artificial intelligence algorithm to solve the synaesthesia performance optimization target, which is a complex non-convex optimization problem of continuous-discrete mixed variables, to obtain an optimal set of correlation and power allocation.

2. The method according to claim 1, characterized in that The pinching antenna link channel model is established in S1, specifically including: Let antenna l and the position of user i in the mth cluster be ψ m,i =(x m,i ,y m,i ,0), where d represents the height of the antenna. Each cluster of users includes strong users s and weak users w. Based on the spherical wave channel model, the channel gain from antenna l to user U(m,i) is expressed as: Among them, i∈{s,w}, τ represents the spherical wave parameter, e is the natural base, j represents the imaginary unit, π is an irrational number, λ represents the wavelength, and ||·|| represents the norm operation.

3. The method according to claim 2, characterized in that The pinching antenna link transmission signal model is established in S1, specifically including: Due to multiple pinching antennas on the same waveguide, the signals of all users are transmitted in a superimposed manner. The signal of M clusters of users is expressed as: Where M represents the total number of user clusters, and the power allocation parameter within the cluster is x m,i , i∈{s,w} represents the communication signal sent to U(m,i).

4. The method according to claim 3, characterized in that: The S3 specifically includes: According to the serial interference cancellation rule of non-orthogonal multiple access NOMA, the data rates of weak user U(m,w) and strong user U(m,s) are expressed as: Among them, the correlation α between the flexible antenna l and the user cluster m l,m ∈{0,1},α=(α l,m ) l∈L,m∈M , p m is the allocated power of user cluster m, and the power allocation parameter within the cluster h l,m,w ,h l,m,s are the channel gains of weak users and strong users respectively, θ l is the phase shift of the superimposed signal passing through antenna l, and represent the intra-cluster interference and inter-cluster interference of weak users respectively, represents the inter-cluster interference of strong users, σ 2 represents additive white Gaussian noise; Therefore, the data rate of user cluster m is expressed as R m =R m,w +R m,s ; The detection signal power P of the target direction k The covariance matrix of the transmitted signal vector M = xx H The channel vector h from the sensing target k to the pinching antenna k Decide, expressed as The optimization objective is to maximize the clustered user data rate and the detection signal power of the sensing target: Among them, α represents the correlation vector between antennas and user clusters, which is a discrete variable, η represents the power allocation parameter vector within the cluster, which is a continuous variable, p represents the transmission power vector, which is a continuous variable, M represents the total number of user clusters, K represents the total number of sensing targets, and γ c ≥0,γ s ≥0 represents the regularization coefficient of communication and perception. By introducing the regularization coefficient, the complexity of the model is limited and the generalization ability of the model is improved.

5. The method according to claim 1, characterized in that The artificial intelligence algorithms in S4 include: deep deterministic policy gradient algorithm and reinforcement learning algorithm.

6. A flexible antenna-assisted synaesthesia-fusion NOMA network resource scheduling device, characterized in that: The device comprises: Establish a module to explore the flexible antenna-assisted interaceptive fusion non-orthogonal multiple access NOMA network architecture. The network architecture considers the scenario of multiple pinching antennas on the same waveguide. In this scenario, communication users access the network in the form of clustered non-orthogonal multiple access. In addition, the communication and perception requirements are comprehensively considered, and the line-of-sight transmission link is reconstructed according to the leakage wave characteristics of the pinching antenna. The pinching antenna link channel model and transmission signal model are established. A first building module is used to build a decision variable set, wherein the decision variable set includes: a correlation α between the flexible antenna and the user cluster, a power allocation parameter η of the users in the cluster, and a transmission power p of the communication user and the sensing target; The second construction module is used to comprehensively analyze the user cluster communication data rate and the detection signal power of the perception target, introduce a regularization coefficient according to the pinching antenna link channel model and the transmission signal model, and the decision variable set, and construct a regularized clustering user data rate and perception target detection power model, as well as a synaesthesia performance optimization target; A solution module is used to use an artificial intelligence algorithm to solve the synaesthesia performance optimization target, which is a complex non-convex optimization problem of continuous-discrete mixed variables, to obtain an optimal correlation and power allocation set.

7. The device according to claim 6, characterized in that The establishment module is specifically used for: Let antenna l and the position of user i in the mth cluster be ψ m,i =(x m,i ,y m,i ,0), where d represents the height of the antenna. Each cluster of users includes strong users s and weak users w. Based on the spherical wave channel model, the channel gain from antenna l to user U(m,i) is expressed as: Among them, i∈{s,w}, τ represents the spherical wave parameter, e is the natural base, j represents the imaginary unit, π is an irrational number, λ represents the wavelength, and ||·|| represents the norm operation.

8. The device according to claim 7, characterized in that The establishment module is further specifically used for: Due to multiple pinching antennas on the same waveguide, the signals of all users are transmitted in a superimposed manner. The signal of M clusters of users is expressed as: Where M represents the total number of user clusters, and the power allocation parameter within the cluster is x m,i , i∈{s,w} represents the communication signal sent to U(m,i).

9. The device according to claim 8, characterized in that The second building block is specifically used for: According to the serial interference cancellation rule of non-orthogonal multiple access NOMA, the data rates of weak user U(m,w) and strong user U(m,s) are expressed as: Among them, the correlation α between the flexible antenna l and the user cluster m l,m ∈{0,1},α=(α l,m ) l∈L,m∈M , p m is the allocated power of user cluster m, and the power allocation parameter within the cluster h l,m,w ,h l,m,s are the channel gains of weak users and strong users respectively, θ l is the phase shift of the superimposed signal passing through antenna l, and represent the intra-cluster interference and inter-cluster interference of weak users respectively, represents the inter-cluster interference of strong users, σ 2 represents additive white Gaussian noise; Therefore, the data rate of user cluster m is expressed as R m =R m,w +R m,s ; The detection signal power P of the target direction k The covariance matrix of the transmitted signal vector M = xx H The channel vector h from the sensing target k to the pinching antenna k Decide, expressed as The optimization objective is to maximize the clustered user data rate and the detection signal power of the sensing target: Among them, α represents the correlation vector between antennas and user clusters, which is a discrete variable, η represents the power allocation parameter vector within the cluster, which is a continuous variable, p represents the transmission power vector, which is a continuous variable, M represents the total number of user clusters, K represents the total number of sensing targets, and γ c ≥0,γ s ≥0 represents the regularization coefficient of communication and perception. By introducing the regularization coefficient, the complexity of the model is limited and the generalization ability of the model is improved.

10. The device according to claim 6, characterized in that The artificial intelligence algorithms of the solution module include: deep deterministic policy gradient algorithm and reinforcement learning algorithm.

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