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

Abstract

The invention relates to a semantic communication anti-interference method and system based on a multifunctional intelligent metasurface, and the method comprises a system model design part and a robust optimization algorithm part, and improves the degree of freedom of reconstructing an electromagnetic propagation environment through the flexible regulation and control capability of the multifunctional intelligent metasurface on the phase and amplitude of electromagnetic waves. And meanwhile, actual defects such as hardware power consumption are overcome through an energy collection function. On the basis of the proposed system model, a robust optimization problem with transmission and rate maximization is constructed. In order to solve the optimization problem, a global optimal algorithm based on fast convergence monotone optimization and decoupling second-order cone programming is provided, a global optimal solution can be obtained through fast convergence, and efficient intelligent metasurface semantic communication anti-interference capability obtaining is achieved.

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 super surface. Background Art

[0002] The inherent openness of the wireless channel propagation environment makes wireless communications increasingly vulnerable to security threats such as interference attacks and information theft. Traditional anti-interference and anti-information interception transmission technologies mainly include direct sequence spread spectrum, frequency hopping, adaptive power control, cooperative relay transmission, artificial noise and multi-antenna technology. The above-mentioned secure communication methods are usually limited to the processing of the transceiver end. However, the fading wireless channel is uncontrollable and is also the fundamental path for interference injection and eavesdropping, thus becoming the main limiting factor for improving the security performance of the system.

[0003] Reconfigurable Intelligent Surface (RIS), a digital programmable intelligent metasurface powered by information metamaterials, uses a large number of passive reflective elements integrated on a plane to intelligently configure the wireless propagation environment through software programming, which has great potential and application prospects in terms of improving the ability of wireless communication security transmission. Intelligent metasurfaces can achieve real-time reconfiguration of wireless channel environments and dynamic programming of wireless channels, reduce and eliminate the uncertainty and uncontrollability of the electromagnetic environment, and provide important means to support the improvement of wireless communication anti-interference and anti-interception capabilities.

[0004] In traditional wireless digital communication systems, passive RIS acts as a relay node to manage and utilize channels in a refined manner to improve the anti-interference performance of wireless communication. Traditional wireless digital communication systems ignore practical problems such as passive RIS hardware power loss and "double fading" path loss. Passive RIS only regulates the phase of reflected electromagnetic waves, and has low degrees of freedom in reconstructing full-space wireless channels, making it difficult to completely block the propagation of interference signals while enhancing the quality of communication signals. 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] In order 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 working constraint and maximum transmission power limit of each receiver in the wireless digital communication system based on the 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;

[0009] A robust optimization problem for maximizing transmission and rate under full-band suppression interference is established for a 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.

[0010] For the robust optimization problem, the interference channel angle error is converted into a robust form using a generalized discretization algorithm, and then the quasi-convex objective function is processed using a fast-converging monotone optimization algorithm and the coupling variables are processed using a decoupled second-order cone programming algorithm, which quickly converges 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, used to obtain the similarity constraint, transmission rate limit, sustainable working constraint of the multifunctional intelligent metasurface and maximum transmission power limit of each receiver in the wireless digital communication system based on the 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;

[0014] The optimization problem module is used to establish a robust optimization problem for maximizing transmission and rate under full-band suppression interference for a 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;

[0015] The problem solving module 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 fast-converging monotone optimization algorithm is used to process the quasi-convex objective function and the decoupled second-order cone programming algorithm is used to process the coupling variables, so as to quickly converge 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 the processor implements the steps of the above-mentioned semantic communication anti-interference method based on the multifunctional intelligent metasurface when executing the computer program.

[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 above-mentioned semantic communication anti-interference method and system based on multifunctional intelligent metasurface, through the system model analysis of the wireless digital communication system based on the multifunctional intelligent metasurface, establishes a robust optimization problem of maximizing transmission and rate under full-band suppression interference. Under the conditions of satisfying the transmission rate limit of each receiver, the sustainable working constraint of the multifunctional intelligent metasurface and the maximum transmission power limit, the focus is on how to achieve the maximization of transmission and rate without the accurate recognition of the interference information state information by jointly optimizing the transmit and receive digital beamforming vector 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 the generalized discretization algorithm, a low-complexity algorithm is proposed, namely, a fast-converging monotone optimization algorithm to process the quasi-convex objective function, and a decoupled second-order cone programming algorithm is used to process the coupling variables, so as to quickly converge to the global optimal solution and configure the wireless digital communication system based on the multifunctional intelligent metasurface accordingly, so as to quickly improve 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 drawings required for use in the embodiments or the conventional technology descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

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

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

[0024] Figure 3 It is a schematic diagram of the iterative process of the fast-converging monotone optimization algorithm in the first 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 scheme and advantages of the present invention clearer, 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 generally understood by those skilled in the art of the present invention. The terms used in the specification 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 article 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 at 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 appreciated by those skilled in the art that the embodiments described herein may be combined with other embodiments. The term "and / or" used in the specification of the present invention 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 will describe the implementation of the present invention in detail with reference to the accompanying drawings in the embodiment diagram of the present invention.

[0029] like Figure 1 In the application environment shown, the system model of the wireless digital communication system based on the multifunctional intelligent metasurface may include a transmitter, a MF-RIS (multi-functional-RIS, multifunctional intelligent metasurface) and K receivers (such as communication vehicles, etc.), and may also include R jammers. Both ends of the transmitter and the 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 full-band high-power suppression interference to try to interrupt the communication link, and the multifunctional intelligent metasurface is used as a relay node to reconstruct the wireless channel. The multifunctional intelligent metasurface can include two working modes, namely energy collection mode (called E mode) and signal relay mode (called R mode). Each RIS unit of the multifunctional intelligent metasurface can flexibly switch between the two working modes through the equipped switching 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 is composed of N = N1 × N2 RIS units, N1 and N2 are the number of RIS units evenly placed along both 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 both 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 of maximizing transmission and rate under full-band suppression interference is established. Under the conditions of satisfying the transmission rate limit of each receiver, the sustainable working constraint of the multifunctional intelligent metasurface, and the maximum transmission power limit, the paper focuses on how to achieve the maximization of transmission and rate without the premise of accurate cognition of interference information state information by jointly optimizing the transmit and receive digital beamforming vector 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 the generalized discretization algorithm, a low-complexity algorithm, namely the fast convergence monotone optimization algorithm (also known as the fast convergence MO algorithm), is proposed to process the quasi-convex objective function, and the optimization problem with the following D2 and D3 is solved by the decoupling second order cone program (DSOCP) algorithm (i.e., processing coupling variables), so as to quickly converge to the global optimal solution to obtain the jointly optimized transmit and receive digital beamforming vector and the coefficient matrix of the multifunctional intelligent metasurface, so as to configure the wireless digital communication system based on the multifunctional intelligent metasurface accordingly, so that it can obtain higher 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 multifunctional intelligent metasurface sustainable working constraint, and a 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;

[0036] S12, establish a robust optimization problem for maximizing transmission and rate under full-band suppression interference for a 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;

[0037] S14, 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;

[0038] S16, configuring 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.

[0039] It can be understood that the multifunctional intelligent metasurface is deployed between transceivers to reconstruct wireless channels. The multifunctional intelligent metasurface can be used 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 suppression interference is established, and the coefficient matrix of the digital beamforming vector for transmission and reception and the multifunctional intelligent metasurface is jointly optimized to maximize the transmission and rate under the similarity constraints of each receiver, the transmission rate limit, the sustainable working constraints of the multifunctional intelligent metasurface, and the maximum transmission power limit. This robust optimization problem can be expressed mathematically as:

[0041]

[0042] Among them, v k represents the digital beamforming vector received by the kth receiver, w k represents the transmitter's transmitted digital beamforming vector, 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 interference channel angle uncertainty range, 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 Respectively 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 indicates that the beamforming vector normalization constraint is accepted. Constraints C5-C7 specify the adjustable range of the coefficients of the multifunctional intelligent metasurface. Constraint C8 is the sustainable working 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, which is difficult to directly process using existing optimization algorithms for bit communication anti-interference. Specifically, the currently widely used continuous convex approximation algorithm (SCA) and majorization-minimization algorithm (MM) 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 a convex function and the other half is a concave function, 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: The multifunctional smart metasurface introduces additional RIS thermal noise in the constraints and objective functions, which makes the robust optimization problem (1) non-convex and difficult to solve. In addition, the nonlinear energy harvesting model and discrete mode switching variables turn 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 interference signal will exist in the denominator of the objective function expression. And the angular uncertainty of the interference channel state information will not only turn the objective function into a maximum-minimum problem, but also introduce infinite non-convexity in the constraints.

[0048] Therefore, a global optimal algorithm based on fast-converging monotone optimization and decoupled second-order cone programming is proposed for the model of the problem mentioned above. Firstly, the interference channel state information error in D4 is converted into a robust form by a generalized discrete algorithm. Then, a fast-converging monotone optimization algorithm is proposed to process the quasi-convex objective function in D1. Finally, the 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-converging 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 numbers. 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, the min in the robust optimization problem (1) is Δ Can be removed, there are:

[0052]

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

[0054] Next, 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 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 collection If ψ satisfies both and Then ψ is The 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 Therefore, this embodiment proposes a novel fast-converging monotonic optimization algorithm, 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 solution in the set, and finally converges to the global optimal solution.

[0065] Furthermore, the calculation steps of the fast convergence 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 an 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-to-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 per word. Therefore, The upper bound b0 can be set to In the formula, 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 modulus-one constraint of the phase of the multifunctional intelligent metasurface.

[0074] Step (2), box set selection: In the lth iteration, select the box set from the current iteration. 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 them until a feasible lower bound a is found. l Cassette collection.

[0075] Step (3), intersection search: In order to narrow down 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] Among them, χ represents Any point on Then use binary search and Pareto bound The 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 noted 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-like 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 ) d ,d=1,…,K (8)

[0080] The superscript d represents b l The dth element in 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 cassette 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 set 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. The iterative process of the first iteration is as follows: Figure 3 shown.

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

[0090] In steps (2) and (3) of the fast convergence monotone optimization algorithm mentioned above, it is necessary to check the fixed point τ (e.g., 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 converts 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 of 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] In the formula, 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 various optimization variables of 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 Translates 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 replaced by an inequality Transformed into the following convex constraints:

[0101]

[0102] in,

[0103] In the non-convex constraints After becoming a convex constraint, the present embodiment continues to process the mixed integer nonlinear programming problem in the optimization problem (15) constraint C5. First, the binary discrete constraint C5 is converted into two continuous constraints, namely: However, 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 terms Convert to in,

[0108] Due to the formula The nonlinear energy harvesting model in is a logistic function, so the multifunctional intelligent metasurface sustainable working constraint in the optimization problem (15) is 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 is introduced 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 So 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 Translates to:

[0117]

[0118] Among them, {ζ 1,k ,ζ 2,r ,ζ3} denotes auxiliary variables. 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:

[0121]

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

[0123] The above semantic communication anti-interference method based on multifunctional intelligent metasurface, through the system model analysis of the wireless digital communication system based on the multifunctional intelligent metasurface, establishes a robust optimization problem of maximizing transmission and rate under full-band suppression interference. Under the conditions of satisfying the transmission rate limit of each receiver, the sustainable working constraint of the multifunctional intelligent metasurface and the maximum transmission power limit, the paper focuses on how to achieve the maximization of transmission and rate without the premise of accurate cognition of interference information state information by jointly optimizing the transmit and receive digital beamforming vector 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 the generalized discretization algorithm, a low-complexity algorithm is proposed, namely, a fast-converging monotone optimization algorithm to process the quasi-convex objective function, and a decoupled second-order cone programming algorithm is used to process the coupling variables, so as to quickly converge to the global optimal solution and configure the wireless digital communication system based on the multifunctional intelligent metasurface accordingly, so as to quickly improve the anti-interference performance of the wireless digital communication system.

[0124] Compared with the existing RIS relay communication anti-interference system model, the proposed system model uses the multifunctional intelligent metasurface to flexibly control the phase and amplitude of electromagnetic waves to improve the freedom of reconstructing the electromagnetic propagation environment, and overcomes practical defects such as hardware power consumption through energy harvesting. 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-converging monotonic optimization and decoupled second-order cone programming is proposed, which can quickly converge to the global optimal solution and realize efficient semantic communication anti-interference capability acquisition.

[0125] It should be understood that although Figure 2 The steps in the method are shown in sequence as indicated by the arrows, but the 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 the steps, and the 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 executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequentially, but can be executed 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, which may include a constraint acquisition module 11, an optimization problem module 13, a problem solving module 15 and an optimization configuration module 17. Among them, the constraint acquisition module 11 is used to obtain the similarity constraint, transmission rate limit, multifunctional intelligent metasurface sustainable working constraint and maximum transmission power limit of each receiver in the wireless digital communication system based on the 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. The optimization problem module 13 is used to 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 coefficient matrix of the receiving and transmitting digital beamforming vector and the multifunctional intelligent metasurface. The problem solving module 15 is used for the robust optimization problem, after converting the interference channel angle error into a robust form using a generalized discretization algorithm, using a fast convergence monotone optimization algorithm to process the quasi-convex objective function and using a decoupled second-order cone programming algorithm to process the coupling variables, and quickly converge to the global optimal solution. The optimization configuration module 17 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.

[0127] The semantic communication anti-interference system 100 based on the multifunctional intelligent metasurface, through the system model analysis of the wireless digital communication system based on the multifunctional intelligent metasurface, establishes a robust optimization problem of maximizing transmission and rate under full-band suppression interference. Under the conditions of satisfying the transmission rate limit of each receiver, the sustainable working constraint of the multifunctional intelligent metasurface and the maximum transmission power limit, the focus is on studying how to achieve the maximization of transmission and rate without the premise of accurate cognition of interference information state information by jointly optimizing the transmit and receive digital beamforming vector 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 is proposed, namely, a fast-converging monotone optimization algorithm to process the quasi-convex objective function, and a decoupled second-order cone programming algorithm is used to process the coupling variables, so as to quickly converge to the global optimal solution and configure the wireless digital communication system based on the multifunctional intelligent metasurface accordingly, so as to quickly improve the anti-interference performance of the wireless digital communication system.

[0128] Compared with the existing RIS relay communication anti-interference system model, the proposed system model uses the multifunctional intelligent metasurface to flexibly control the phase and amplitude of electromagnetic waves to improve the freedom of reconstructing the electromagnetic propagation environment, and overcomes practical defects such as hardware power consumption through energy harvesting. 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-converging monotonic optimization and decoupled second-order cone programming is proposed, which can quickly converge to the global optimal solution and realize efficient semantic communication anti-interference capability acquisition.

[0129] In one embodiment, the calculation steps of the fast convergence 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 an 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 above robust optimization problem is:

[0135]

[0136] Among them, v k represents the digital beamforming vector received by the kth receiver, wk represents the transmitter's transmitted digital beamforming vector, 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 interference channel angle uncertainty range, 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 Respectively 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 limitations of the semantic communication anti-interference system 100 based on the multifunctional intelligent supersurface, please refer to the corresponding limitations of the semantic communication anti-interference method based on the multifunctional intelligent supersurface above, which will not be repeated here. Each module in the above-mentioned semantic communication anti-interference system 100 based on the multifunctional intelligent supersurface can be fully or partially implemented 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, including 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 similarity constraints, transmission rate limits, sustainable working constraints of the multifunctional intelligent metasurface, and maximum transmission power limits 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; a robust optimization problem for maximizing transmission and rate under full-band suppression interference is established 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 quasi-convex objective function is processed using a fast convergence monotonic optimization algorithm and a coupling variable is processed through a decoupled second-order cone programming algorithm to quickly converge to a global optimal solution; 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 vectors 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, and when the computer program is executed by a processor, the following processing steps are implemented: obtaining similarity constraints, transmission rate limits, sustainable working constraints of the multifunctional intelligent metasurface, and maximum transmission power limits 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; establishing a robust optimization problem for maximizing transmission and rate under full-band suppressed interference for a 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; for the robust optimization problem, after converting the interference channel angle error into a robust form using a generalized discretization algorithm, the quasi-convex objective function is processed using a fast convergence monotonic optimization algorithm and the coupling variables are processed through a decoupled second-order cone programming algorithm to quickly converge to a global optimal solution; 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 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 in each embodiment of the above-mentioned semantic communication anti-interference method based on the multifunctional intelligent metasurface.

[0142] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed 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-mentioned 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. As an illustration and not limitation, RAM is available in many 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 (RambusDRAM, referred to as RDRAM) and interface dynamic random access memory (DRDRAM).

[0143] The technical features of the above embodiments may 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 only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of protection of the invention. It should be pointed out that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the attached claims.

Claims

1. A semantic communication anti-interference method based on a multifunctional intelligent metasurface, characterized in that: Includes steps: Obtaining similarity constraints, transmission rate limits, sustainable working constraints of the multifunctional intelligent metasurface, and maximum transmission power limits 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; 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 coefficient matrix of the transmit and receive digital beamforming vectors and the multifunctional intelligent metasurface; For the robust optimization problem, after the interference channel angle error is converted 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, so as to quickly converge to the global optimal solution. 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.

2. The semantic communication anti-interference method based on the multifunctional intelligent metasurface according to claim 1 is characterized in that: 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 an 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.

3. The semantic communication anti-interference method based on the multifunctional intelligent metasurface according to claim 1 or 2 is characterized in that: The robust optimization problem is: Where vk represents the kth receiver receiving digital beamforming vector, wk represents the transmitter transmitting digital beamforming vector, 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 interference channel angle uncertainty range, S k (γ k ) represents the transmission semantic rate of the kth receiver, ξ(γ k ) represents the semantic similarity of the kth receiver, ξth represents the minimum similarity threshold, Sth represents the minimum transmission rate threshold, represents the mode switching coefficient of the multifunctional intelligent metasurface, Pmax represents the maximum transmission power of the transmitter, and P H and P T ot 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.

4. A semantic communication anti-interference system based on a multifunctional intelligent metasurface, characterized in that: include: A constraint acquisition module, used to obtain the similarity constraint, transmission rate limit, sustainable working 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, used to establish a robust optimization problem of 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 coefficient matrix of the transmit and receive digital beamforming vector and the multifunctional intelligent metasurface; A problem-solving module is used 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, so as to quickly converge to a global optimal solution. The optimization configuration module is used to configure the communication of the wireless digital communication system based on the multifunctional intelligent supersurface according to the optimized transmit and receive digital beamforming vectors and the coefficient matrix of the multifunctional intelligent supersurface in the global optimal solution.

5. The semantic communication anti-interference system based on the multifunctional intelligent metasurface according to claim 4 is characterized in that: 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 an 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.

6. The semantic communication anti-interference system based on the multifunctional intelligent metasurface according to claim 4 or 5, characterized in that: The robust optimization problem is: Where vk represents the kth receiver receiving digital beamforming vector, wk represents the transmitter transmitting digital beamforming vector, 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 interference channel angle uncertainty range, S k (γ k ) represents the transmission semantic rate of the kth receiver, ξ(γ k ) represents the semantic similarity of the kth receiver, ξth represents the minimum similarity threshold, Sth represents the minimum transmission rate threshold, represents the mode switching coefficient of the multifunctional intelligent metasurface, Pmax represents the maximum transmission power of the transmitter, and P H and P T ot 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.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the semantic communication anti-interference method based on the multifunctional intelligent metasurface according to any one of claims 1 to 3 are implemented.

8. 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 any one of claims 1 to 3 are implemented.

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