Semantic communication anti-interference method and system based on multifunctional intelligent metasurface

Abstract

The present invention relates to a semantic communication anti-interference method and system based on a multifunctional intelligent metasurface, which includes two parts: system model design and robust optimization algorithm. The flexible control capability of the multifunctional intelligent metasurface on the phase and amplitude of electromagnetic waves is utilized to enhance the degree of freedom in reconstructing the electromagnetic propagation environment. At the same time, the energy harvesting function is used to overcome practical defects such as hardware power consumption. Based on the proposed system model, a robust optimization problem for maximizing transmission and rate is constructed. In order to solve the above optimization problem, a global optimal algorithm based on fast-convergence monotonic optimization and decoupled second-order cone programming is proposed. The algorithm can quickly converge to the global optimal solution and realize efficient acquisition of the semantic communication anti-interference capability of the intelligent metasurface.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communications and relates to a semantic communication anti-interference method and system based on a multifunctional intelligent metasurface. Background Art

[0002] The inherent openness of wireless channel propagation environments makes wireless communications increasingly vulnerable to security threats such as interference attacks and information theft. Traditional anti-interference and anti-interception transmission technologies primarily include direct sequence spread spectrum, frequency hopping, adaptive power control, cooperative relay transmission, artificial noise, and multi-antenna technology. These secure communication methods are generally limited to transceiver processing. However, fading wireless channels are uncontrollable and provide a fundamental path for interference injection and eavesdropping, thus becoming a major limiting factor in improving system security performance.

[0003] Digitally programmable intelligent metasurfaces (RIS), powered by information metamaterials, utilize a large number of passive reflective elements integrated on a flat surface to intelligently configure the wireless propagation environment through software programming. These metasurfaces hold enormous potential and promise for improving the secure transmission of wireless communications. They enable real-time reconfiguration and dynamic programming of wireless channel environments, reducing and eliminating the uncertainty and uncontrollability of the electromagnetic environment. This provides a crucial tool for enhancing the anti-interference and anti-interception capabilities of wireless communications.

[0004] In traditional wireless digital communication systems, passive RIS (Remotely Infrared Radio Frequency Interference) (RIS) acts as a relay node, providing refined channel management and utilization to enhance wireless communication anti-interference performance. However, these systems ignore practical issues such as passive RIS hardware power loss and "double-fading" path loss. Passive RIS only manipulates the phase of reflected electromagnetic waves, limiting its ability to reconstruct the full-space wireless channel. This makes it difficult to completely block the propagation of interfering signals while simultaneously enhancing communication signal quality. Summary of the Invention

[0005] In response to the problems existing in the above-mentioned traditional methods, the present invention proposes a semantic communication anti-interference method based on a multifunctional intelligent metasurface, a semantic communication anti-interference system based on a multifunctional intelligent metasurface, a computer device and a computer-readable storage medium, which can effectively improve the anti-interference performance of wireless digital communication systems.

[0006] To achieve the above objectives, the embodiments of the present invention adopt the following technical solutions:

[0007] On the one hand, a semantic communication anti-interference method based on a multifunctional intelligent metasurface is provided, comprising the steps of:

[0008] Obtaining the similarity constraint, transmission rate limit, sustainable operation constraint, and maximum transmission power limit of each receiver in a wireless digital communication system based on a multifunctional intelligent metasurface; the wireless digital communication system based on a multifunctional intelligent metasurface includes a transmitter, a multifunctional intelligent metasurface, a receiver, and a jammer;

[0009] A robust optimization problem is established for maximizing transmission and rate under full-band interference suppression for a wireless digital communication system based on a multifunctional intelligent metasurface. This robust optimization problem is used to jointly optimize the transmit and receive digital beamforming vectors and the coefficient matrix of the multifunctional intelligent metasurface.

[0010] For the robust optimization problem, the interference channel angle error is converted into a robust form using a generalized discretization algorithm. Then, a fast-converging monotone optimization algorithm is used to process the quasi-convex objective function and a decoupled second-order cone programming algorithm is used to process the coupling variables, quickly converging to the global optimal solution.

[0011] The communication configuration of the wireless digital communication system based on the multifunctional intelligent metasurface is performed according to the optimized transmit and receive digital beamforming vectors and the coefficient matrix of the multifunctional intelligent metasurface in the global optimal solution.

[0012] On the other hand, a semantic communication anti-interference system based on a multifunctional intelligent metasurface is also provided, comprising:

[0013] A constraint acquisition module is used to obtain the similarity constraint, transmission rate limit, sustainable operation constraint, and maximum transmit power limit of each receiver in a wireless digital communication system based on a multifunctional intelligent metasurface. The wireless digital communication system based on a multifunctional intelligent metasurface includes a transmitter, a multifunctional intelligent metasurface, a receiver, and a jammer.

[0014] An optimization problem module is used to establish a robust optimization problem for maximizing transmission and rate under full-band interference suppression for a wireless digital communication system based on a multifunctional intelligent metasurface. This robust optimization problem is used to jointly optimize the transmit and receive digital beamforming vectors and the coefficient matrix of the multifunctional intelligent metasurface.

[0015] The problem-solving module is used to solve robust optimization problems. After converting the interference channel angle error into a robust form using a generalized discretization algorithm, it uses a fast-converging monotone optimization algorithm to process the quasi-convex objective function and a decoupled second-order cone programming algorithm to process the coupled variables, quickly converging to the global optimal solution.

[0016] The optimization configuration module is used to configure the communication of the wireless digital communication system based on the multifunctional intelligent metasurface according to the optimized transmit and receive digital beamforming vectors and the coefficient matrix of the multifunctional intelligent metasurface in the global optimal solution.

[0017] On the other hand, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned semantic communication anti-interference method based on the multifunctional intelligent metasurface are implemented.

[0018] On the other hand, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned semantic communication anti-interference method based on the multifunctional intelligent metasurface are implemented.

[0019] One of the above technical solutions has the following advantages and beneficial effects:

[0020] The aforementioned semantic communication anti-interference method and system based on a multifunctional intelligent metasurface analyzes the system model of a wireless digital communication system based on the multifunctional intelligent metasurface and establishes a robust optimization problem for maximizing transmission and rate under full-band interference suppression. While satisfying the transmission rate constraints of each receiver, the sustainable operation constraints of the multifunctional intelligent metasurface, and the maximum transmit power limit, the method focuses on how to achieve transmission and rate maximization without requiring accurate knowledge of interference information by jointly optimizing the transmit and receive digital beamforming vectors and the coefficient matrix of the multifunctional intelligent metasurface. A global optimal algorithm for the proposed model is proposed. After converting the interference channel angle error into a robust form using a generalized discretization algorithm, a low-complexity algorithm, namely a fast-converging monotonic optimization algorithm, is proposed to handle the quasi-convex objective function. A decoupled second-order cone programming algorithm is then used to handle the coupled variables. This method rapidly converges to the global optimal solution and configures the wireless digital communication system based on the multifunctional intelligent metasurface accordingly, thereby rapidly improving the semantic communication anti-interference performance of the wireless digital communication system. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the conventional technology, the following briefly introduces the drawings required for use in the embodiments or the conventional technology descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0022] Figure 1 A schematic diagram of an application environment of a system model in one embodiment;

[0023] Figure 2 1 is a flow chart of a method for semantic communication anti-interference based on a multifunctional intelligent metasurface in one embodiment;

[0024] Figure 3 FIG1 is a schematic diagram of the first iteration process of a fast-converging monotonic optimization algorithm in one embodiment;

[0025] Figure 4 Schematic diagram of the module architecture of a semantic communication anti-interference system based on a multifunctional intelligent metasurface in one embodiment. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and Examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the description of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0027] It should be noted that the reference to "embodiment" in this document means that the specific features, structures or characteristics described in conjunction with the embodiment may be included in at least one embodiment of the present invention. The presentation of this phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It will be understood by those skilled in the art that the embodiments described herein may be combined with other embodiments. The term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0028] The following describes the embodiments of the present invention in detail with reference to the accompanying drawings.

[0029] like Figure 1 In the application environment shown, the system model of the wireless digital communication system based on the multifunctional intelligent metasurface can include a transmitter, a MF-RIS (multi-functional-RIS, multifunctional intelligent metasurface) and K receivers (such as communication vehicles, etc.), and can also include R jammers. Both ends of the transmitter and receiver can be equipped with semantic communication modules to further compress the data volume and improve reliable transmission under low signal-to-noise ratio, thereby increasing the communication rate.

[0030] The jammer uses high-power suppression across the entire frequency band to disrupt the communication link, while the multifunctional intelligent metasurface acts as a relay node to reconstruct the wireless channel. The multifunctional intelligent metasurface can operate in two modes: energy harvesting mode (E-mode) and signal relay mode (R-mode). Each RIS unit on the multifunctional intelligent metasurface can flexibly switch between these two modes using a mode switch.

[0031] The multifunctional intelligent metasurface divides the entire three-dimensional space into a reflection area and a transmission area. For the convenience of subsequent discussion, the receiver set in the reflection area is represented as KR ∈{1,2,…,K R}, the receiver set in the transmission area is represented as K T ∈{K R +1,K R +2,…,K R +K T}, where K = K R +K T Assume that the multifunctional smart metasurface consists of N = N1 × N2 RIS units, where N1 and N2 are the number of RIS units evenly placed along the sides of the multifunctional smart metasurface, and the transmitter and K receivers are equipped with active uniform planar antenna arrays (UPA) of sizes M = M1 × M2 and L = L1 × L2, respectively, where M1 / L1 and M2 / L2 are the number of units evenly placed along the sides of the active uniform planar antenna array.

[0032] In addition, in order to achieve global interference with low hardware cost and complexity, it is assumed that each of the R jammers uses a single omnidirectional antenna to interfere with the receiver. represents the channel coefficient from the transmitter to the multifunctional intelligent metasurface, represents the channel coefficient from the multifunctional intelligent metasurface to the kth receiver (where k∈(1,2,…,K)), represents the channel coefficient from the transmitter to the kth receiver, represents the channel coefficient from the rth jammer to the multifunctional intelligent metasurface and represents the channel coefficient from the rth interferer to the kth receiver.

[0033] A robust optimization problem for maximizing transmission and rate under full-band interference suppression is established. While satisfying the transmission rate constraints of each receiver, the sustainable operation constraints of the multifunctional intelligent metasurface, and the maximum transmit power limit, the authors focus on jointly optimizing the transmit and receive digital beamforming vectors and the coefficient matrices of the multifunctional intelligent metasurface to achieve transmission and rate maximization without requiring accurate knowledge of the interference state. A global optimal algorithm for the proposed model is proposed. After transforming the interference channel angle error into a robust form using a generalized discretization algorithm, a low-complexity algorithm, the rapidly converging monotone optimization algorithm (also known as the rapidly converging MO algorithm), is proposed to handle the quasi-convex objective function. The optimization problem with the following D2 and D3 (i.e., handling coupled variables) is solved using a decoupling second-order cone program (DSOCP) algorithm. This algorithm rapidly converges to a global optimal solution, obtaining the jointly optimized transmit and receive digital beamforming vectors and the coefficient matrices of the multifunctional intelligent metasurface. This allows the configuration of a wireless digital communication system based on the multifunctional intelligent metasurface to achieve high semantic communication anti-interference performance.

[0034] In one embodiment, Figure 2 As shown, a semantic communication anti-interference method based on a multifunctional intelligent metasurface is provided, which may include the following processing steps S10 to S16:

[0035] S10, obtaining a similarity constraint, a transmission rate limit, a sustainable operation constraint of the multifunctional intelligent metasurface, and a maximum transmission power limit for each receiver in a wireless digital communication system based on a multifunctional intelligent metasurface; the wireless digital communication system based on a multifunctional intelligent metasurface includes a transmitter, a multifunctional intelligent metasurface, a receiver, and a jammer;

[0036] S12: Establish a robust optimization problem for maximizing transmission and rate under full-band interference suppression for a wireless digital communication system based on a multifunctional intelligent metasurface. This robust optimization problem is used to jointly optimize the transmit and receive digital beamforming vectors and the coefficient matrix of the multifunctional intelligent metasurface.

[0037] S14, for the robust optimization problem, uses a generalized discretization algorithm to convert the interference channel angle error into a robust form, then uses a fast-converging monotone optimization algorithm to process the quasi-convex objective function and a decoupled second-order cone programming algorithm to process the coupling variables, quickly converging to the global optimal solution;

[0038] S16, configuring the wireless digital communication system based on the multifunctional intelligent metasurface according to the optimized transmit and receive digital beamforming vectors and the coefficient matrix of the multifunctional intelligent metasurface in the global optimal solution.

[0039] It can be understood that the multifunctional intelligent metasurface is deployed between transceivers to reconstruct wireless channels. It can use the multifunctional intelligent metasurface to improve the freedom of reconstructing the full-space interference electromagnetic propagation environment, while overcoming practical defects such as RIS hardware power consumption and "double fading" path loss.

[0040] A robust optimization problem for maximizing transmission and rate under full-band interference suppression is established. The transmit and receive digital beamforming vectors and the coefficient matrix of the multifunctional intelligent metasurface are jointly optimized to maximize the transmission and rate while satisfying the similarity constraints of each receiver, the transmission rate limit, the sustainable operation constraint of the multifunctional intelligent metasurface, and the maximum transmit power limit. This robust optimization problem can be mathematically expressed as:

[0041]

[0042] Among them, v k represents the digital beamforming vector received by the kth receiver, w k represents the digital beamforming vector transmitted by the transmitter, Φ R represents the reflection angle control coefficient matrix of the multifunctional intelligent metasurface, Φ T represents the transmission diagonal control coefficient matrix of the multifunctional intelligent metasurface, Δ represents the uncertainty range of the interference channel angle, S k (γ k ) represents the transmission semantic rate of the kth receiver, ξ(γ k ) represents the semantic similarity of the kth receiver, ξ th Represents the minimum similarity threshold, S th Indicates the minimum transmission rate threshold, represents the mode switching coefficient of the multifunctional smart metasurface, P max Indicates the maximum transmission power of the transmitter, P H and P Tot Represent the total power of collected energy and the total power consumed, α R,n represents the reflection amplitude coefficient, α T,n represents the transmission amplitude coefficient, α max represents the maximum amplitude control coefficient of the functional intelligent metasurface unit. Among them, the RF power input to the energy harvesting module is The output power consumption of the multifunctional intelligent metasurface can be expressed as Constraints C1 and C2 ensure that each receiver can successfully decode the information. Constraint C3 ensures that the total transmit power is less than the maximum transmit power. Constraint C4 accepts the normalization constraint of the beamforming vector. Constraints C5-C7 specify the adjustable range of the coefficients of the multifunctional intelligent metasurface. Constraint C8 is the sustainable operation constraint of the multifunctional intelligent metasurface.

[0043] The specific reasons why the above robust optimization problem (1) is difficult to solve directly are as follows:

[0044] D1: The objective function in the robust optimization problem (1) is a generalized logistic function with quasi-convexity, making it difficult to directly use existing optimization algorithms for bit communication anti-interference. Specifically, the currently widely used continuous convex approximation (SCA) and majorization-minimization (MM) algorithms usually introduce auxiliary variables or find lower bound functions to approximate the objective function. However, the objective function of the robust optimization problem (1) is quasi-convex, that is, half of the objective function is convex and the other half is concave, making it difficult to introduce appropriate auxiliary variables and lower bound functions for approximation.

[0045] D2: Multiple coupled high-dimensional optimization variables make the robust optimization problem (1) difficult to solve directly. Although the Block Coordinate Descent (BCD) algorithm can simplify the original optimization problem by iteratively solving each optimization variable, it may fall into a local optimal solution.

[0046] D3: Multifunctional smart metasurfaces introduce additional RIS thermal noise into the constraints and objective functions, which results in the robust optimization problem (1) being non-convex and difficult to solve. In addition, the nonlinear energy harvesting model and discrete mode switching variables transform the robust optimization problem (1) into a mixed integer nonlinear programming (MINLP) problem, which further increases the difficulty of solving the problem.

[0047] D4: The interfering signal will appear in the denominator of the objective function expression. Furthermore, the angular uncertainty of the interfering channel state information not only transforms the objective function into a maximum-minimum problem but also introduces infinite non-convexity into the constraints.

[0048] Therefore, a global optimal algorithm based on fast-convergence monotone optimization and decoupled second-order cone programming is proposed for the model of the problem mentioned above. First, a generalized discrete algorithm is used to convert the interference channel state information error in D4 into a robust form. Then, a fast-convergence monotone optimization algorithm is proposed to process the quasi-convex objective function in D1. Finally, a decoupled second-order cone programming algorithm is used to solve the optimization problem with D2 and D3 to obtain the global optimal solution.

[0049] Fast convergence monotone optimization algorithm: The objective function and constraints of the robust optimization problem (1) have infinite non-convexity caused by Δ (i.e., min Δ), a generalized discretization algorithm is used to convert the non-ideal CSI (channel state information, i.e., the interference channel angle error) of the interference information into an equivalent robust form. First, the elevation angle θ in Δ (p) and azimuth Uniform discretization is:

[0050]

[0051] Among them, Q1 and Q2 represent the number of sampling points of angle error, θ L Indicates reaching the lower limit of the elevation angle, represents the lower bound of the arrival azimuth angle, Δθ represents the arrival elevation angle sampling interval, represents the arrival azimuth sampling interval, i and j represent the sampling sequence number. When Q1 and Q2 tend to infinity, the discretized error set approaches the continuous set Δ. Therefore, the worst-case robust interference channel state information and Can be expressed as and in, and represents the channel vector obtained by formula (2). After the above robustification process, the min in the robust optimization problem (1) is Δ Can be removed:

[0052]

[0053] in, Indicates that the parameters in C are replaced by and The expression after .

[0054] Then, for the quasi-convex objective function in D1, use the variable Replace the transmission rate S in the original optimization problem (3) k , and then transform the original optimization problem (3) into:

[0055]

[0056] In the equation, g(τ) is expressed as the objective function with τ as the independent variable, and its domain is:

[0057]

[0058] in, and

[0059] In order to solve the optimization problem (4), we first give four definitions of the proposed fast-converging monotone optimization algorithm:

[0060] Definition 1: Given two vectors The set [a,b] = [τ|a≤τ≤b] with vertices a and b is a box set.

[0061] Definition 2: Given a box set If there is a vector satisfy It must be a well-ordered set on [a,b].

[0062] Definition 3: Define a non-empty well-ordered set and in A monotonically increasing function If the optimization problem can be equivalent to Then this optimization problem must be a monotonic optimization problem.

[0063] Definition 4: Define a fixed point and a set If ψ satisfies both and Then ψ is Upper bound of And the global optimal solution of a monotone optimization problem must be superior.

[0064] According to the above definitions 1-4, we can find that the optimization problem (4) is a well-ordered set The monotone optimization problem on , and can be solved by searching the upper bound To find the global optimal solution. Therefore, this embodiment proposes a novel fast-converging monotonic optimization algorithm, which constructs a set of box sets containing the global optimal solution, iteratively reduces the size of the box set by judging the feasibility of the solutions in the set, and finally converges to the global optimal solution.

[0065] Furthermore, the calculation steps of the fast-converging monotone optimization algorithm may include:

[0066] Initialize a box set containing all objective function values;

[0067] Perform box set selection in the current iteration calculation;

[0068] Perform intersection search in the current iteration calculation to obtain the updated box set;

[0069] After resetting the box set according to the updated box set, jump to the next iterative calculation and loop until the iteration is terminated when the difference between the upper and lower bounds of the objective function value is less than the preset convergence value.

[0070] Specifically, the specific calculation steps of the fast convergence monotone optimization algorithm can be given as follows:

[0071] Step (1), initialization: First initialize a box set containing all objective function values Among them, the upper and lower bounds of the objective function value can be set as g max (b0) and g min (a0). Obviously, the box set The lower bound a0 can be directly set as the minimum transmission rate threshold, that is, Assuming that the transmitter communicates only with the kth receiver and ignores interference signals, the maximum transmission rate of the kth receiver can be obtained as in, represents the maximum signal-to-interference-and-noise ratio of the kth receiver, W represents the transmission bandwidth, I represents the average amount of information contained in the original sentence (unit: Semantic Units, suts), and ρ represents the average number of characters in each word. Therefore, The upper bound b0 can be set to Where, It can be calculated by the following inequality:

[0072]

[0073] Among them, inequality (a) holds due to the Cauchy-Schwarz inequality, inequality (b) holds due to the triangle inequality, and inequality (c) holds due to the modulo-one constraint of the phase of the multifunctional intelligent metasurface.

[0074] Step (2), box set selection: In the lth iteration calculation, select the box set from the Choose a l )=g min and g(b l )=g max The box set [a l ,b l ], and check the lower bound a l feasibility. If the lower bound a l is not a feasible solution, then [a l ,b l ]from Eliminate until a feasible lower bound a is found l Box collection.

[0075] Step (3), intersection search: In order to reduce the box set [a l ,b l ], we need to improve the lower bound of the box set by searching for intersections. is the lower bound a l and upper bound b l The connection between them is:

[0076]

[0077] Where χ represents Any point on Then use binary search and Pareto bound intersection Finally, given the search accuracy, the range of the intersection point [ψ min,l ,ψ max,l ], and update g min =g(ψ min,l ). It should be pointed out that if g(ψ min,l )≤g(a l ), then discard the intersection point ψ l And reset g min =g(a l ).

[0078] Step (4), box set splitting: In order to reduce the upper bound of the box set, [a l ,b l ] is split into K non-overlapping box subsets, whose upper boundary vertex set {b l,d} can be expressed as:

[0079] b l,d =b l -(b l,d -ψ l,d )e d ,d=1,…,K (8)

[0080] Where, the superscript d represents b l The dth element in, e d Represents a vector whose dth element is 1 and the rest of the elements are 0. In addition, the vertex set of the lower boundary corresponding to the upper boundary is updated as follows:

[0081]

[0082] The K box subsets after splitting need to meet the following conditions:

[0083]

[0084] New box subset [a l,d ,b l,d ] has no intersection with the previous box set and has a larger upper bound g(b l,d ). Finally, the box collection Updated to:

[0085]

[0086] Step (5), box set reset: Due to the updated box set It is necessary to satisfy that any value of the upper bound is greater than the corresponding lower bound. If it is not satisfied, the subbox set is infeasible and should be directly eliminated. Otherwise, the lower edge of the box set can be reset. Specifically, if g(b l+1 )≤g min , then [a l+1 ,b l+1 ]from Remove, otherwise a l+1 Reset to:

[0087]

[0088] Finally, when the difference between the upper and lower bounds g max -g min When the convergence value is less than the preset convergence value, the fast convergence monotone optimization algorithm terminates, and the iterative process of the lth time is as follows: Figure 3 shown.

[0089] Next, we introduce the feasibility test based on the decoupled second-order cone programming algorithm:

[0090] In the steps (2) and (3) of the fast convergence monotone optimization algorithm mentioned above, it is necessary to check the fixed point τ (such as the lower bound vertex a in step (2)). l and the intersection point ψ in step (3) l ), the optimization problem can be expressed as:

[0091]

[0092] As shown in D2, The high-dimensional optimization variables wk, Φ in R and Φ T The high degree of coupling makes the optimization problem (13) difficult to solve. To address this problem, this embodiment proposes a decoupled second-order cone programming algorithm: it first Decoupling and The two parts are then solved using the Linear Minimum-Mean-Square-Error (LMMSE) criterion and the Second-Order Cone Programming (SOCP) algorithm respectively. and

[0093] According to the LMMSE guidelines, The optimal closed-form solution is:

[0094]

[0095] in, and σ1 represents the white noise power received by the receiver, σ2 represents the MF-RIS thermal noise power, I L Represents the identity matrix of dimension L×L.

[0096] Next, solve The optimization problem can be expressed as:

[0097]

[0098] Where, C ρ,1 represents the logistic growth rate, C ρ,2 Represents the logistic midpoint coefficient, A ρ,1 represents the left asymptote, A ρ,2 represents the right asymptote. However, in the optimization problem (15) The optimization variables are still highly coupled in the constraints. Therefore, the second-order cone programming algorithm is used to deal with the coupled variables in the constraints. First, the auxiliary variables are introduced. The constraints Converts to:

[0099]

[0100] in, Because of non-convex terms and the mutually coupled variables in the optimization variables, constraints is still non-convex. Therefore, the constraint condition is transformed into Converted into the following convex constraint:

[0101]

[0102] in,

[0103] In the non-convex constraints After the constraint condition is changed to convex, the mixed integer nonlinear programming problem in the optimization problem (15) constraint condition C5 is processed in this embodiment. First, the binary discrete constraint condition C5 is converted into two continuous constraints, namely: However, It is still a non-convex term. Using the inequality, Approximately its upper bound Based on this, the non-convex constraint C5 can be converted into a convex constraint

[0104]

[0105] In addition, in order to deal with the non-convex mutual coupling term in the constraint C7 of the optimization problem (15) Introducing auxiliary variables The C7 equivalent is expressed as:

[0106]

[0107] Then, the non-convex items Convert to in,

[0108] Because the formula The nonlinear energy harvesting model in is a logistic function, so the sustainable working constraints of the multifunctional intelligent metasurface in the optimization problem (15) are Here, we first set the constraint The equivalent conversion is:

[0109]

[0110] Where η represents the energy coefficient, p S 、p DC and p E They represent the power consumed by each phase shifter, the DC bias power of the amplifier, and the power consumed by the RF to DC converter. Then, the auxiliary variable and ζ=p I ,have:

[0111]

[0112] Obviously, the constraints The right side of is non-convex. Using the first-order Taylor expansion Convert to its lower bound Thus, the constraints The equivalent conversion is:

[0113]

[0114] In addition, the introduction of auxiliary variables Approximate p O , constraints It can be further expressed as:

[0115]

[0116] Next, the constraints Converts to:

[0117]

[0118] Among them, {ζ 1,k ,ζ 2,r ,ζ3} represents an auxiliary variable. However, and The optimization variables in are non-convex and highly coupled. To solve this problem, and Convert to a solvable form:

[0119]

[0120] Finally, using the above mathematical transformation and introducing the penalty coefficient κ2>0, the constraint condition Combined with the objective function, the original optimization problem (15) is transformed into a solvable form, which is:

[0121]

[0122] in, The optimization problem (27) is a second-order cone programming problem that can be solved using the existing CVX solver. Finally, the feasibility of τ can be tested by determining whether the constraints in the optimization problem (15) are satisfied.

[0123] This semantic communication anti-interference method based on a multifunctional intelligent metasurface analyzes the system model of a wireless digital communication system based on a multifunctional intelligent metasurface and establishes a robust optimization problem for maximizing transmission and rate under full-band interference suppression. While satisfying the transmission rate constraints of each receiver, the sustainable operation constraints of the multifunctional intelligent metasurface, and the maximum transmit power limit, the method focuses on jointly optimizing the transmit and receive digital beamforming vectors and the coefficient matrix of the multifunctional intelligent metasurface to achieve transmission and rate maximization without requiring accurate knowledge of the interference state. A global optimal algorithm for the proposed model is proposed. After converting the interference channel angle error into a robust form using a generalized discretization algorithm, a low-complexity algorithm, namely a fast-converging monotone optimization algorithm, is proposed to handle the quasi-convex objective function. A decoupled second-order cone programming algorithm is then used to handle the coupled variables. This method rapidly converges to a global optimal solution and configures the wireless digital communication system based on the multifunctional intelligent metasurface accordingly, thereby rapidly improving the anti-interference performance of the wireless digital communication system.

[0124] Compared to existing RIS relay communication anti-interference system models, the proposed system model leverages the multifunctional intelligent metasurface's ability to flexibly control the phase and amplitude of electromagnetic waves, increasing the freedom to reconstruct the electromagnetic propagation environment. It also overcomes practical limitations such as hardware power consumption through energy harvesting. Based on the proposed system model, a robust optimization problem for maximizing transmission and rate was constructed. To solve this optimization problem, a global optimal algorithm based on fast-converging monotonic optimization and decoupled second-order cone programming was proposed. This algorithm rapidly converges to the global optimal solution, enabling efficient acquisition of semantic communication anti-interference capabilities.

[0125] It should be understood that although Figure 2 The steps in the diagram are shown in the order indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Figure 2 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0126] In one embodiment, Figure 4 As shown, a semantic communication anti-interference system 100 based on a multifunctional intelligent metasurface is provided. The system may include a constraint acquisition module 11, an optimization problem module 13, a problem solving module 15, and an optimization configuration module 17. The constraint acquisition module 11 is used to obtain the similarity constraint, transmission rate limit, sustainable operation constraint of the multifunctional intelligent metasurface, and maximum transmit power limit for each receiver in a wireless digital communication system based on a multifunctional intelligent metasurface. The wireless digital communication system based on a multifunctional intelligent metasurface includes a transmitter, a multifunctional intelligent metasurface, a receiver, and a jammer. The optimization problem module 13 is used to establish a robust optimization problem for maximizing transmission and rate under full-band interference suppression for the wireless digital communication system based on a multifunctional intelligent metasurface. The robust optimization problem is used to jointly optimize the transmit and receive digital beamforming vectors and the coefficient matrix of the multifunctional intelligent metasurface. The problem solving module 15 is used to solve the robust optimization problem. After converting the interference channel angle error into a robust form using a generalized discretization algorithm, the problem solving module 15 uses a fast-converging monotone optimization algorithm to process the quasi-convex objective function and a decoupled second-order cone programming algorithm to process the coupled variables, thereby rapidly converging to a global optimal solution. The optimization configuration module 17 is used to configure the wireless digital communication system based on the multifunctional intelligent metasurface according to the optimized transmit and receive digital beamforming vectors and the coefficient matrix of the multifunctional intelligent metasurface in the global optimal solution.

[0127] The aforementioned semantic communication anti-interference system 100 based on a multifunctional intelligent metasurface analyzes the system model of a wireless digital communication system based on a multifunctional intelligent metasurface and establishes a robust optimization problem for maximizing transmission and rate under full-band interference suppression. While satisfying the transmission rate limit of each receiver, the sustainable operation constraint of the multifunctional intelligent metasurface, and the maximum transmit power limit, the system focuses on jointly optimizing the transmit and receive digital beamforming vectors and the coefficient matrix of the multifunctional intelligent metasurface to achieve transmission and rate maximization without requiring accurate knowledge of interference information. A global optimal algorithm for the proposed model is proposed. After converting the interference channel angle error into a robust form using a generalized discretization algorithm, a low-complexity algorithm, namely a fast-converging monotone optimization algorithm, is proposed to handle the quasi-convex objective function. A decoupled second-order cone programming algorithm is then used to handle the coupled variables. This method rapidly converges to a global optimal solution, which is then used to configure the wireless digital communication system based on the multifunctional intelligent metasurface, thereby rapidly improving the anti-interference performance of the wireless digital communication system.

[0128] Compared to existing RIS relay communication anti-interference system models, the proposed system model leverages the multifunctional intelligent metasurface's ability to flexibly control the phase and amplitude of electromagnetic waves, increasing the freedom to reconstruct the electromagnetic propagation environment. It also overcomes practical limitations such as hardware power consumption through energy harvesting. Based on the proposed system model, a robust optimization problem for maximizing transmission and rate was constructed. To solve this optimization problem, a global optimal algorithm based on fast-converging monotonic optimization and decoupled second-order cone programming was proposed. This algorithm rapidly converges to the global optimal solution, enabling efficient acquisition of semantic communication anti-interference capabilities.

[0129] In one embodiment, the calculation steps of the fast-converging monotonic optimization algorithm may include:

[0130] Initialize a box set containing all objective function values;

[0131] Perform box set selection in the current iteration calculation;

[0132] Perform intersection search in the current iteration calculation to obtain the updated box set;

[0133] After resetting the box set according to the updated box set, jump to the next iterative calculation and loop until the iteration is terminated when the difference between the upper and lower bounds of the objective function value is less than the preset convergence value.

[0134] In one embodiment, the robust optimization problem is:

[0135]

[0136] Among them, v k represents the digital beamforming vector received by the kth receiver, wk represents the digital beamforming vector transmitted by the transmitter, Φ R represents the reflection angle control coefficient matrix of the multifunctional intelligent metasurface, Φ T represents the transmission diagonal control coefficient matrix of the multifunctional intelligent metasurface, Δ represents the uncertainty range of the interference channel angle, S k (γ k ) represents the transmission semantic rate of the kth receiver, ξ(γ k ) represents the semantic similarity of the kth receiver, ξ th Represents the minimum similarity threshold, S th Indicates the minimum transmission rate threshold, represents the mode switching coefficient of the multifunctional smart metasurface, P max Indicates the maximum transmission power of the transmitter, P H and P Tot Represent the total power of collected energy and the total power consumed, α R,n represents the reflection amplitude coefficient, α T,n represents the transmission amplitude coefficient, α max Represents the maximum amplitude control coefficient of the functional intelligent metasurface unit, and C1 to C8 are all constraints.

[0137] Regarding the specific definition of the semantic communication anti-interference system 100 based on the multifunctional intelligent metasurface, please refer to the corresponding definition of the semantic communication anti-interference method based on the multifunctional intelligent metasurface above, which will not be repeated here. Each module in the above-mentioned semantic communication anti-interference system 100 based on the multifunctional intelligent metasurface can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of a device with data processing functions in the form of hardware, or can be stored in the memory of the aforementioned device in the form of software, so that the processor can call and execute the operations corresponding to the above modules. The aforementioned device can be, but is not limited to, various types of wireless communication computer devices already available in the art.

[0138] In one embodiment, a computer device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the following processing steps when executing the computer program: obtaining a similarity constraint, a transmission rate limit, a multifunctional intelligent metasurface sustainable working constraint, and a maximum transmission power limit for each receiver in a wireless digital communication system based on a multifunctional intelligent metasurface; the wireless digital communication system based on the multifunctional intelligent metasurface includes a transmitter, a multifunctional intelligent metasurface, a receiver, and a jammer; establishing a robust optimization problem for maximizing transmission and rate under full-band suppressed interference for the wireless digital communication system based on the multifunctional intelligent metasurface; the robust optimization problem is used to jointly optimize the transmit and receive digital beamforming vector and the coefficient matrix of the multifunctional intelligent metasurface; for the robust optimization problem, after converting the interference channel angle error into a robust form using a generalized discretization algorithm, a fast-convergence monotonic optimization algorithm is used to process the quasi-convex objective function and a decoupled second-order cone programming algorithm is used to process the coupling variables, and the global optimal solution is quickly converged; and the wireless digital communication system based on the multifunctional intelligent metasurface is configured for communication according to the optimized transmit and receive digital beamforming vector and the coefficient matrix of the multifunctional intelligent metasurface in the global optimal solution.

[0139] In one embodiment, when the processor executes the computer program, it can also implement the steps or sub-steps added in each embodiment of the above-mentioned semantic communication anti-interference method based on the multifunctional intelligent metasurface.

[0140] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which implements the following processing steps when executed by a processor: obtaining similarity constraints, transmission rate limits, sustainable working constraints of the multifunctional intelligent metasurface, and maximum transmission power limits for each receiver in a wireless digital communication system based on a multifunctional intelligent metasurface; the wireless digital communication system based on the multifunctional intelligent metasurface includes a transmitter, a multifunctional intelligent metasurface, a receiver, and a jammer; establishing a robust optimization problem for maximizing transmission and rate under full-band suppressed interference for the wireless digital communication system based on the multifunctional intelligent metasurface; the robust optimization problem is used to jointly optimize the transmit and receive digital beamforming vectors and the coefficient matrix of the multifunctional intelligent metasurface; for the robust optimization problem, after converting the interference channel angle error into a robust form using a generalized discretization algorithm, a fast-convergence monotonic optimization algorithm is used to process the quasi-convex objective function and a decoupled second-order cone programming algorithm is used to process the coupling variables, and the global optimal solution is quickly converged; the wireless digital communication system based on the multifunctional intelligent metasurface is configured for communication according to the optimized transmit and receive digital beamforming vectors and the coefficient matrix of the multifunctional intelligent metasurface in the global optimal solution.

[0141] In one embodiment, when the computer program is executed by a processor, it can also implement the steps or sub-steps added to the above-mentioned embodiments of the semantic communication anti-interference method based on the multifunctional intelligent metasurface.

[0142] Those skilled in the art will appreciate that all or part of the processes in the above-described embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-described methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus dynamic random access memory (Rambus DRAM, abbreviated as RDRAM) and interface dynamic random access memory (DRDRAM).

[0143] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0144] The above embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that a person skilled in the art would be able to make numerous modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of the present invention. Therefore, the scope of the present invention shall be determined by the appended claims.

Claims

1. A semantic communication anti-interference method based on a multifunctional intelligent metasurface, characterized in that: Including steps: Obtaining a similarity constraint, a transmission rate limit, a sustainable operation constraint of the multifunctional intelligent metasurface, and a maximum transmission power limit for each receiver in a wireless digital communication system based on a multifunctional intelligent metasurface; the wireless digital communication system based on the multifunctional intelligent metasurface includes a transmitter, a multifunctional intelligent metasurface, a receiver, and a jammer; Establish a robust optimization problem for maximizing transmission and rate under full-band suppression interference for the wireless digital communication system based on the multifunctional intelligent metasurface; The robust optimization problem is used to jointly optimize the transmit and receive digital beamforming vectors and the coefficient matrix of the multifunctional intelligent metasurface; For the robust optimization problem, after converting the interference channel angle error into a robust form using a generalized discretization algorithm, a fast-converging monotone optimization algorithm is used to process the quasi-convex objective function and a decoupled second-order cone programming algorithm is used to process the coupling variables, quickly converging to the global optimal solution. Performing communication configuration on the wireless digital communication system based on the multifunctional intelligent metasurface according to the optimized transmit and receive digital beamforming vectors and the coefficient matrix of the multifunctional intelligent metasurface in the global optimal solution; The calculation steps of the fast convergence monotone optimization algorithm include: Initialize a box set containing all objective function values; Perform box set selection in the current iteration calculation; Perform intersection search in the current iteration calculation to obtain the updated box set; After resetting the box set according to the updated box set, jump to the next iterative calculation and loop until the iteration is terminated when the difference between the upper and lower bounds of the objective function value is less than the preset convergence value.

2. The semantic communication anti-interference method based on the multifunctional intelligent metasurface according to claim 1 is characterized in that: The robust optimization problem is: in, Indicates the k The receiver receives the digital beamforming vector, represents the digital beamforming vector transmitted by the transmitter, represents the reflection angle control coefficient matrix of the multifunctional intelligent metasurface, represents the transmission diagonal control coefficient matrix of the multifunctional intelligent metasurface, represents the uncertainty range of the interference channel angle, Indicates the k The transmission semantic rate of each receiver, Indicates the k The semantic similarity of the receivers, represents the minimum similarity threshold, Indicates the minimum transmission rate threshold, represents the mode switching coefficient of the multifunctional smart metasurface, Indicates the maximum transmission power of the transmitter. Represent the total power of collected energy and the total power consumed, represents the reflection amplitude coefficient, represents the transmission amplitude coefficient, Represents the maximum amplitude control coefficient of the functional intelligent metasurface unit, and C1 to C8 are all constraints.

3. A semantic communication anti-interference system based on a multifunctional intelligent metasurface, characterized in that: include: A constraint acquisition module is used to obtain the similarity constraint, transmission rate limit, sustainable operation constraint of the multifunctional intelligent metasurface, and maximum transmission power limit of each receiver in a wireless digital communication system based on a multifunctional intelligent metasurface; the wireless digital communication system based on a multifunctional intelligent metasurface includes a transmitter, a multifunctional intelligent metasurface, a receiver, and a jammer; An optimization problem module, for establishing a robust optimization problem for maximizing transmission and rate under full-band suppression interference for the wireless digital communication system based on the multifunctional intelligent metasurface; The robust optimization problem is used to jointly optimize the transmit and receive digital beamforming vectors and the coefficient matrix of the multifunctional intelligent metasurface; A problem-solving module is used to convert the interference channel angle error into a robust form using a generalized discretization algorithm for the robust optimization problem, process the quasi-convex objective function using a fast-converging monotone optimization algorithm, and process the coupling variables using a decoupled second-order cone programming algorithm to quickly converge to a global optimal solution; An optimization configuration module, configured to configure the wireless digital communication system based on the multifunctional intelligent metasurface according to the optimized transmit and receive digital beamforming vectors and the coefficient matrix of the multifunctional intelligent metasurface in the global optimal solution; The calculation steps of the fast convergence monotone optimization algorithm include: Initialize a box set containing all objective function values; Perform box set selection in the current iteration calculation; Perform intersection search in the current iteration calculation to obtain the updated box set; After resetting the box set according to the updated box set, jump to the next iterative calculation and loop until the iteration is terminated when the difference between the upper and lower bounds of the objective function value is less than the preset convergence value.

4. The semantic communication anti-interference system based on the multifunctional intelligent metasurface according to claim 3 is characterized in that: The robust optimization problem is: in, Indicates the k The receiver receives the digital beamforming vector, represents the digital beamforming vector transmitted by the transmitter, represents the reflection angle control coefficient matrix of the multifunctional intelligent metasurface, represents the transmission diagonal control coefficient matrix of the multifunctional intelligent metasurface, represents the uncertainty range of the interference channel angle, Indicates the k The transmission semantic rate of each receiver, Indicates the k The semantic similarity of the receivers, represents the minimum similarity threshold, Indicates the minimum transmission rate threshold, represents the mode switching coefficient of the multifunctional smart metasurface, Indicates the maximum transmission power of the transmitter. Represent the total power of collected energy and the total power consumed, represents the reflection amplitude coefficient, represents the transmission amplitude coefficient, Represents the maximum amplitude control coefficient of the functional intelligent metasurface unit, and C1 to C8 are all constraints.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the semantic communication anti-interference method based on the multifunctional intelligent metasurface according to claim 1 or 2 are implemented.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the semantic communication anti-interference method based on the multifunctional intelligent metasurface according to claim 1 or 2 are implemented.

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