STAR-RIS-assisted secure transmission method for maximizing weighted and confidentiality rates in cellless networks

Abstract

This invention discloses a secure transmission method for maximizing weighted sum security in a cell-free network assisted by STAR-RIS, belonging to the field of wireless communication technology. The method first obtains complete channel state information of the Cell-Free downlink network, initializes the base station beamforming vector and STAR-RIS transmission / reflection coefficients satisfying coupling constraints, constructs received signal models for users and eavesdroppers, calculates the signal-to-interference-plus-noise ratio (SINR), and establishes a weighted sum security maximization optimization problem. A continuous convex approximation method is used to linearize the non-convex objective function, decomposing the problem into two sub-problems: beamforming optimization and STAR-RIS coefficient optimization, which are solved alternately. The beamforming sub-problem is solved using convex optimization tools, while the STAR-RIS coefficient sub-problem uses the augmented Lagrangian method to handle coupling constraints and derive a closed-form solution. The optimal parameters are obtained through iterative convergence. This invention strictly adheres to the actual physical constraints of STAR-RIS, has low computational complexity, can simultaneously serve users on both sides of the surface, and significantly improves the secure transmission performance and spectral efficiency of cell-free networks.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, specifically relating to a secure transmission method that maximizes weighted and confidentiality rates in a STAR-RIS-assisted cellless network. Background Technology

[0002] With the development of 6G, intelligent wireless coverage, and IoT communication, future wireless networks not only require higher spectrum efficiency and coverage capabilities but also place higher demands on information transmission security. Due to the inherent broadcast nature of wireless channels, while legitimate users receive signals, unauthorized nodes within the coverage area may also receive the same or related signals, making wireless systems vulnerable to eavesdropping attacks. This is especially true in cell-free networks with multi-user, multi-access-point collaborative services, where multiple distributed base stations jointly provide downlink transmission to users. While this effectively reduces cell boundary effects, improves user experience at the edge, and increases system capacity, the more open network topology and more complex signal propagation paths allow potential eavesdroppers to access more spatial locations near effective transmission links, thus posing more severe physical layer security challenges to the system.

[0003] To improve the wireless propagation environment and enhance system performance, reconfigurable smart surfaces (RIS) technology has received widespread attention in recent years. Traditional RISs reconstruct the propagation wavefront by manipulating the phase of reflective elements, thereby enhancing the desired signal, suppressing interference, and improving coverage. However, traditional RISs typically only possess reflective capabilities, limiting their service area to one side of the plane on which the RIS is located, making it difficult to simultaneously meet the transmission needs of users on both sides of the RIS. In cell-free networks, the spatial distribution of users and potential eavesdroppers is often quite dispersed. Relying solely on traditional reflective RISs cannot fully leverage the advantages of smart surfaces in controlling the entire spatial propagation environment, thus limiting further improvements in secure transmission performance.

[0004] Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) can simultaneously divide incident signals into transmitted and reflected portions, enabling them to cover users on both sides of the RIS. This provides higher spatial freedom and stronger channel shaping capabilities compared to traditional RIS. Therefore, introducing STAR-RIS into cell-free networks is expected to enhance the reception quality for legitimate users while further suppressing eavesdropping links, improving system weighting and security. However, many existing studies often employ idealized assumptions in modeling and solving, assuming that the transmission / reflection amplitudes and phases of each STAR-RIS unit can be independently and losslessly adjusted. In practical low-cost passive lossless devices, however, the transmission and reflection processes must satisfy energy conservation, and there is usually inherent coupling between the transmission and reflection phases. This means that the transmission / reflection coefficients of STAR-RIS cannot be designed completely independently as in ideal models. Ignoring these practical coupling constraints, while theoretically achieving higher optimization results, is difficult to implement in real devices and may even lead to significant deviations between algorithm design results and actual system performance.

[0005] On the other hand, in STAR-RIS-assisted Cell-Free networks, to achieve secure transmission, it is usually necessary to jointly optimize the beamforming vectors of distributed base stations and the transmission / reflection coefficients of STAR-RIS. Since both the legitimate user information rate and the eavesdropper information rate are highly coupled with beamforming variables, STAR-RIS coefficients, and link propagation structure, and given the amplitude and phase coupling constraints inherent in STAR-RIS itself, the resulting weighted summation and security maximization problem is a high-dimensional, strongly coupled, and non-convex optimization problem. Some existing methods only consider optimizing a subset of these variables, such as optimizing only base station beamforming or only RIS phase shift, or simplifying the model through overly idealized independent phase shift assumptions, making it difficult to simultaneously consider practical constraints and system security performance. Other methods, while considering more constraints, rely on complex numerical iterations in their solution process, resulting in high computational complexity and making them unsuitable for real-time or near-real-time deployments in multi-base station, multi-user, and dynamic channel environments.

[0006] Therefore, existing technologies suffer from at least the following problems: First, they ignore the actual coupling constraints of STAR-RIS, leading to overly idealized models and insufficient engineering applicability; second, they fail to effectively combine base station beamforming and STAR-RIS parameter design in Cell-Free scenarios, resulting in limited improvements in security performance; and third, the solution complexity for coupling constraints and non-convex targets is high, making it difficult for the algorithm to balance optimization performance and implementation efficiency. Based on this, there is an urgent need for a method that can simultaneously consider the actual physical constraints of STAR-RIS, is applicable to Cell-Free network security transmission scenarios, and can efficiently solve for maximizing the weighted sum and security rate, in order to effectively improve the system's security capabilities and practical deployment feasibility. Summary of the Invention

[0007] The technical problem to be solved by this invention is to provide a secure transmission method that maximizes weighted sum and confidentiality in a STAR-RIS-assisted cellless network, addressing the shortcomings of the prior art. This method solves the technical problems of neglecting actual coupling constraints, high complexity of optimization algorithms, and limited system security performance in existing STAR-RIS research.

[0008] The present invention adopts the following technical solution: A STAR-RIS-assisted secure transmission method for maximizing weighted and confidentiality rates in cell-free networks includes the following steps: S1. Obtain the channel state information of the STAR-RIS assisted Cell-Free downlink network, and initialize the beamforming vector of each base station and the transmission / reflection coefficients of STAR-RIS; wherein, the transmission / reflection coefficients include amplitude coefficients and phase shift coefficients, and satisfy the coupling constraints between transmission and reflection; S2. Based on the channel state information, beamforming vector and STAR-RIS transmission / reflection coefficients obtained in step S1, construct the received signal models of the user and the eavesdropper, calculate the signal-to-interference-plus-noise ratio of the user and the signal-to-interference-plus-noise ratio of the eavesdropper intercepting the user's information, and determine the user's confidentiality rate based on the difference between the user's information rate and the eavesdropper's information rate, and establish an optimization problem with the goal of maximizing the weighted sum confidentiality rate. S3. For the optimization problem established in step S2, the objective function is linearized using the continuous convex approximation method. Given the optimization variables of the current iteration, the user information rate and the eavesdropper information rate are transformed into convex lower bound approximate expressions about the current iteration point through first-order Taylor expansion, thus obtaining an approximate problem that can be solved by convex optimization. S4. Using the linearized objective function obtained in step S3, under the condition of fixed STAR-RIS transmission / reflection coefficients, the approximate problem is transformed into a convex optimization subproblem about the beamforming vector of each base station, and the updated beamforming vector is obtained by solving it under the transmit power limit of each base station. S5. Using the beamforming vector obtained in step S4, optimize the STAR-RIS transmission / reflection coefficients under the condition of fixed beamforming vectors. For the coupling constraints of the STAR-RIS transmission / reflection coefficients, introduce auxiliary variables and use the augmented Lagrangian method to transform the equality constraints into penalty terms in the objective function. Update the amplitude coefficients and phase shift coefficients of STAR-RIS by alternate optimization. S6. Substitute the updated STAR-RIS transmission / reflection coefficients from step S5 and the updated beamforming vector from step S4 into the objective function of step S2, calculate the weighted sum and security rate of the current iteration, and determine whether the convergence condition is met. If it is met, output the optimized beamforming vector and STAR-RIS transmission / reflection coefficients. If it is not met, return to step S3 to continue the iteration.

[0009] Preferably, in step S1, the channel state information includes: Channel state information from each base station to each user, channel state information from each base station to each eavesdropper, channel state information from each base station to STAR-RIS, channel state information from STAR-RIS to each user, and channel state information from STAR-RIS to each eavesdropper. The transmission / reflection coefficients of STAR-RIS include transmission amplitude coefficient, reflection amplitude coefficient, transmission phase shift coefficient, and reflection phase shift coefficient.

[0010] Preferably, the transmission amplitude coefficient and the reflection amplitude coefficient satisfy the energy splitting constraint, and the transmission phase shift coefficient and the reflection phase shift coefficient satisfy the phase coupling constraint; The energy splitting constraint is used to characterize the distribution relationship of incident signal energy between the transmission and reflection directions of the STAR-RIS unit, and the phase coupling constraint is used to characterize the correlation relationship between the transmission phase and the reflection phase of the same STAR-RIS unit.

[0011] Preferably, in step S2, when constructing the received signal models of the user and the eavesdropper, the received signal of the user and the received signal of the eavesdropper are determined according to the confidential signals sent by each base station, the beamforming vector of each base station, the direct link channel, and the cascaded link channel formed by STAR-RIS. Based on the expected signal power, interference signal power, and noise power in the user's received signal and the eavesdropper's received signal, the signal-to-interference-plus-noise ratio (SIR) of the user and the SIR of the eavesdropper intercepting the user's information are calculated respectively.

[0012] Preferably, in step S2, the user information rate is determined based on the user's signal-to-interference-plus-noise ratio (SIR), the eavesdropper information rate is determined based on the SIR of the user's information intercepted by the eavesdropper, and the difference between the user information rate and the eavesdropper information rate is used as the user's confidentiality rate. Based on the weights corresponding to each user, the confidentiality rates of each user are summed in a weighted manner to obtain the weighted sum confidentiality rate, and the optimization problem is established with the goal of maximizing the weighted sum confidentiality rate.

[0013] Preferably, in step S3, when linearizing the user information rate and the eavesdropper information rate, the optimization variable at the current iteration point is used as the expansion point, and the non-convex terms in the user information rate and the eavesdropper information rate are expanded by first-order Taylor expansion to obtain an approximate expression for the convex lower bound about the current iteration point; and the non-convex objective function term in the original optimization problem is replaced by the approximate expression for the convex lower bound to obtain the approximate problem.

[0014] Preferably, in step S4, under the condition of fixed STAR-RIS transmission / reflection coefficients, the equivalent channels of the user and the eavesdropper are determined as fixed parameters, and the approximation problem is transformed into a convex optimization subproblem with the beamforming vectors of each base station as optimization variables;

[0015] in, It is the set of beamforming vectors for all base stations. for Number of users in the region For regional identification, Index for users, For information rate, For the first In the next iteration Region 1 The weight of each user for Region 1 The weight of each user For the first The coefficients of the first-order term after linearizing the user information rate in the next iteration. The coefficients of the first-order term after linearizing the user information rate. For interference and noise terms in the eavesdropper's information rate, For the first The approximate term after linearizing the eavesdropper's information rate in the next iteration. The conjugate transpose of the beamforming matrix. for Region 1 Equivalent channel matrix for each user This is the conjugate transpose of the equivalent channel matrix. For the first Beamforming matrix of the next iteration For the first The maximum transmit power of each base station For base station indexing, The total number of base stations. For the first base stations Region 1 Beamforming vectors for each user; The constraints of the convex optimization subproblem include at least the transmit power limit of each base station. The updated beamforming vector of each base station is obtained by solving the convex optimization subproblem.

[0016] Preferably, in step S5, under the condition of a fixed beamforming vector, the optimization problem of the STAR-RIS transmission / reflection coefficient is transformed into a STAR-RIS coefficient optimization sub-problem with the STAR-RIS amplitude coefficient and phase shift coefficient as optimization variables; To address the coupling constraints of the STAR-RIS transmission / reflection coefficients, auxiliary variables and Lagrange dual variables are introduced to construct an augmented Lagrange function, and the equality constraints corresponding to the coupling constraints are transformed into penalty terms in the augmented Lagrange function.

[0017] Preferably, when performing alternating optimization on the STAR-RIS coefficient optimization subproblem, the STAR-RIS coefficient optimization subproblem is decomposed into a phase shift coefficient optimization subproblem and an amplitude coefficient optimization subproblem; Given the amplitude coefficient, solve the phase shift coefficient optimization subproblem to obtain the updated phase shift coefficient; Given the updated phase shift coefficients, solve the amplitude coefficient optimization subproblem to obtain the updated amplitude coefficients; The updated STAR-RIS transmission / reflection coefficients are determined based on the updated phase shift coefficients and the updated amplitude coefficients.

[0018] Secondly, embodiments of the present invention provide a secure transmission system for maximizing weighted and confidentiality rates in a STAR-RIS-assisted cellless network, comprising: The channel information acquisition and initialization module is used to acquire the channel state information of the STAR-RIS assisted Cell-Free downlink network and initialize the beamforming vector of each base station and the transmission / reflection coefficients of STAR-RIS; wherein the transmission / reflection coefficients include amplitude coefficients and phase shift coefficients, and satisfy the coupling constraints between transmission and reflection; The receiving model construction module is used to construct the received signal models of the user and the eavesdropper based on the channel state information, beamforming vector and STAR-RIS transmission / reflection coefficient, calculate the signal-to-interference-plus-noise ratio of the user and the signal-to-interference-plus-noise ratio of the eavesdropper intercepting the user's information, determine the user's confidentiality rate based on the difference between the user's information rate and the eavesdropper's information rate, and establish an optimization problem with the goal of maximizing the weighted sum confidentiality rate. The linearization module is used to linearize the objective function for the optimization problem using a continuous convex approximation method. Given the optimization variables of the current iteration, the user information rate and the eavesdropper information rate are transformed into convex lower bound approximate expressions about the current iteration point through a first-order Taylor expansion, thus obtaining an approximate problem that can be solved by convex optimization. The beamforming optimization module is used to transform the approximate problem into a convex optimization subproblem about the beamforming vector of each base station under the condition of fixed STAR-RIS transmission / reflection coefficients by using the linearized objective function obtained by the linearization processing module, and to solve the updated beamforming vector under the transmit power limit of each base station. The STAR-RIS coefficient optimization module is used to optimize the STAR-RIS transmission / reflection coefficients under the condition of a fixed beamforming vector using the beamforming optimization module. For the coupling constraints of the STAR-RIS transmission / reflection coefficients, auxiliary variables are introduced and the augmented Lagrangian method is used to transform the equality constraints into penalty terms in the objective function. The amplitude coefficients and phase shift coefficients of STAR-RIS are updated by alternately optimizing. The convergence judgment and output module is used to substitute the updated STAR-RIS transmission / reflection coefficients and the updated beamforming vector into the objective function of the optimization problem, calculate the weighted sum and security rate of the current iteration, and determine whether the convergence condition is met. If it is met, the optimized beamforming vector and STAR-RIS transmission / reflection coefficients are output. If it is not met, the linearization processing module is triggered to continue the iteration.

[0019] Thirdly, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the secure transmission method for maximizing weighted and confidentiality rates in a STAR-RIS-assisted cellless network described above.

[0020] Fourthly, embodiments of the present invention provide a computer-readable storage medium including a computer program, which, when executed by a processor, implements the steps of the above-described secure transmission method for maximizing weighted and confidentiality rates in a STAR-RIS-assisted cellless network.

[0021] Fifthly, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the secure transmission method for maximizing weighted and confidentiality rates in a STAR-RIS-assisted cellless network described above.

[0022] In a sixth aspect, embodiments of the present invention provide an electronic device including a computer program, which, when executed by the electronic device, implements the steps of the above-described secure transmission method for maximizing weighted and confidentiality rates in a STAR-RIS-assisted cellless network.

[0023] Compared with the prior art, the present invention has at least the following beneficial effects: A secure transmission method for maximizing weighted sum and security in a STAR-RIS-assisted cell-free network is proposed. First, channel state information of the STAR-RIS-assisted cell-free downlink network is obtained, and the base station beamforming vector and STAR-RIS transmission / reflection coefficients are initialized. Then, reception models for legitimate users and eavesdroppers are constructed, establishing an optimization problem with the goal of maximizing the weighted sum and security. Next, the non-convex objective function is linearized using a continuous convex approximation method, and an alternating optimization mechanism is employed to solve for the base station beamforming vector and STAR-RIS transmission / reflection coefficients respectively. Finally, the optimization result is output when the convergence condition is met. By establishing a system model considering actual coupling constraints, the original problem is decomposed into two sub-problems—beamforming and coupling phase shift—using a linearized alternating optimization framework, and a closed-form solution for coupling phase shift is innovatively derived. This method improves the system's weighted sum and security, reduces algorithm computational complexity, and balances high performance with low complexity. Joint optimization significantly improves the system's security performance. Linearization and step-by-step iterative solving reduce the difficulty of solving complex non-convex problems, demonstrating good engineering feasibility.

[0024] Furthermore, the channel state information from each base station to each user, from each base station to each eavesdropper, from each base station to STAR-RIS, from STAR-RIS to each user, and from STAR-RIS to each eavesdropper is defined; at the same time, the STAR-RIS transmission / reflection coefficients are clearly defined, including transmission amplitude coefficient, reflection amplitude coefficient, transmission phase shift coefficient, and reflection phase shift coefficient; the direct links and cascaded links via STAR-RIS are fully covered, making the secure transmission modeling more comprehensive and closer to the real wireless propagation environment; the STAR-RIS parameters are decomposed into two dimensions, amplitude and phase shift, which is beneficial for subsequent handling of energy splitting and phase coupling constraints; and a clear parameter interface definition is provided for moving from abstract algorithms to engineering implementation.

[0025] Furthermore, the transmission amplitude coefficient and reflection amplitude coefficient satisfy the energy splitting constraint, and the transmission phase shift coefficient and reflection phase shift coefficient satisfy the phase coupling constraint, making the system model of the present invention more consistent with the working mechanism of real STAR-RIS devices, thus improving the credibility and engineering applicability of the scheme; from a protection perspective, it highlights the key innovation of the present invention that distinguishes it from the ideal STAR-RIS model; the introduction of coupling constraints drives the design of subsequent augmented Lagrangian and alternating optimization methods.

[0026] Furthermore, based on the confidential signals transmitted by each base station, the beamforming vectors of each base station, the direct link channel, and the cascaded link channel formed by STAR-RIS, the user received signal and the eavesdropper received signal are determined respectively. Based on the expected signal power, interference signal power, and noise power in the received signal, the corresponding signal-to-interference-plus-noise ratio is calculated, and the security optimization is established on a more detailed and physical receiving model. By simultaneously describing the legitimate user link and the eavesdropper link, the optimization target of secure transmission has a bilateral comparison basis. This not only improves the quality of the legitimate link and suppresses illegal interception capabilities, but also incorporates the direct link and the cascaded link into the model, so that the reconfigurable propagation gain brought by STAR-RIS can be explicitly utilized.

[0027] Furthermore, the user information rate is determined based on the user's signal-to-interference-plus-noise ratio (SIR), and the eavesdropper's information rate is determined based on the SIR when the eavesdropper intercepts the user's information. The difference between the two is used as the user's confidentiality rate. This is then combined with the weights of each user to form a weighted sum confidentiality rate target. This directly measures the degree to which information is securely transmitted, better aligning with the theoretical definition of physical layer secure communication. By introducing user weights, in multi-user cell-free scenarios, the priority, fairness, or business needs of different users are considered, making the optimization target more flexible and better suited to actual system scheduling requirements.

[0028] Furthermore, using the optimization variable at the current iteration point as the expansion point, a first-order Taylor expansion is performed on the non-convex terms in the user information rate and the eavesdropper information rate, respectively, to obtain an approximate expression for the convex lower bound of the current iteration point. These expressions are then used to replace the non-convex objective function terms in the original optimization problem, resulting in an approximate problem. By using continuous convex approximation, the original problem is gradually transformed into a locally solvable convex problem, significantly reducing the solution complexity. The convex lower bound approximation method helps ensure that the objective value monotonically improves or stably converges during the iteration process, thereby improving the reliability of the algorithm. Using the current iteration point as the expansion point allows for local corrections based on the latest optimization results in each iteration, improving the solution accuracy.

[0029] Furthermore, under the condition of fixed STAR-RIS transmission / reflection coefficients, the equivalent channels of users and eavesdroppers are treated as fixed parameters, and the approximation problem is transformed into a convex optimization subproblem with the beamforming vectors of each base station as optimization variables. The updated beamforming vector is obtained by solving the problem under the premise of satisfying the transmit power limit of each base station. By fixing the STAR-RIS coefficients, the dimension of the optimization variables is reduced, making the beamforming update more stable. The convex optimization subproblem is easy to implement using mature algorithm frameworks. While retaining the transmit power constraint, it can balance security performance and energy consumption, which meets the actual communication system's constraint requirements on transmit resources.

[0030] Furthermore, under the condition of a fixed beamforming vector, the optimization problem of STAR-RIS transmission / reflection coefficients is transformed into a STAR-RIS coefficient optimization subproblem with amplitude coefficients and phase shift coefficients as optimization variables. Auxiliary variables and Lagrangian dual variables are introduced to address the coupling constraints, constructing an augmented Lagrangian function. The equality constraints corresponding to the coupling constraints are transformed into penalty terms. By introducing auxiliary variables, the originally directly coupled constraint structure is relaxed and reconstructed, making the problem easier to handle step-by-step. The augmented Lagrangian function transfers the difficult equality constraints to the objective function, reducing the difficulty of solving explicit constraints.

[0031] Furthermore, the STAR-RIS coefficient optimization subproblem is decomposed into a phase shift coefficient optimization subproblem and an amplitude coefficient optimization subproblem. The phase shift coefficient optimization subproblem is solved given the amplitude coefficient, and the amplitude coefficient optimization subproblem is solved given the updated phase shift coefficient, thereby determining the updated STAR-RIS transmission / reflection coefficients. This further decouples the internal coupling relationships of the STAR-RIS coefficients, concentrating each optimization step on fewer variables and reducing computational difficulty. The alternating optimization method is simple to implement and has clear iterations, facilitating its integration with the aforementioned augmented Lagrange framework. Optimizing the phase and amplitude separately allows for a more detailed exploration of the STAR-RIS's degrees of freedom in wavefront reconstruction, improving user links and suppressing eavesdropping links while satisfying physical constraints.

[0032] It is understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0033] In summary, this invention addresses the secure transmission problem in STAR-RIS-assisted Cell-Free networks by jointly modeling STAR-RIS parameter optimization under actual coupling constraints with distributed base station beamforming. With the goal of maximizing the weighted sum security rate, it handles complex non-convex constraints through continuous convex approximation, alternating optimization, and augmented Lagrangian methods. This improves the security performance of legitimate users while also considering computational complexity and engineering feasibility.

[0034] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0035] Figure 1 A scene diagram illustrating the application of the method of this invention; Figure 2 This is a schematic diagram illustrating the convergence of the algorithm; Figure 3 This diagram illustrates how the weighted sum secrecy rate (WSSR) varies with the distance between BS and STAR-RIS. Figure 4 A graph showing the relationship between the power budget at WSSR and BS; Figure 5 A schematic diagram of a computer device provided in an embodiment of the present invention; Figure 6 This is a block diagram of a chip according to an embodiment of the present invention; Figure 7 This is a schematic diagram of the method flow of the present invention.

[0036] Among them, 60. Computer equipment; 61. Processor; 62. Memory; 63. Computer program; 600. Electronic device; 610. Processing unit; 620. Storage unit; 6201. Random access memory unit; 6202. Cache memory unit; 6203. Read-only memory unit; 6204. Program / utility; 6205. Program module; 630. Bus; 640. Display unit; 650. Input / output interface; 660. Network adapter; 700. External device. Detailed Implementation

[0037] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0039] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0040] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0041] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0042] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0043] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0044] This invention provides a secure transmission method for maximizing weighted sum and confidentiality in a STAR-RIS-assisted cellless network. It establishes a cellless network model incorporating distributed base stations, STAR-RIS, multiple users, and multiple eavesdroppers, accurately considering the amplitude and phase coupling constraints of STAR-RIS. The secure transmission problem is formulated as a weighted sum and confidentiality maximization problem, providing a theoretical basis for subsequent optimization that conforms to actual hardware characteristics. The original non-convex problem is decomposed into two sub-problems: beamforming optimization and coupled phase shift optimization. A first-order Taylor expansion technique is used to linearize the non-convex objective function, constructing a solvable convex optimization sub-problem to ensure... Each iteration effectively improves system performance. For the coupled phase shifter problem, by constructing an augmented Lagrangian function and an alternating direction multiplier method, complex constrained optimization is transformed into unconstrained optimization. Furthermore, through variable separation and mathematical derivation, closed-form expressions for the optimal solutions of phase and amplitude are obtained, completely avoiding highly complex numerical iterations. Through the above scheme, this invention achieves a significant improvement in system security performance while strictly adhering to the actual physical constraints of STAR-RIS. Simultaneously, it greatly reduces computational complexity through efficient algorithm design, providing a feasible solution for the deployment of STAR-RIS in practical communication systems.

[0045] Please see Figure 1This invention addresses a cell-free downlink communication scenario and provides a beam tracking technology using a millimeter-wave massive MIMO system. The network includes... One antenna base station, one equipped with STAR-RIS components and There are one single-antenna user. In addition, there are two single-antenna eavesdropping devices ( and The controller is responsible for coordinating the transmission / reflection coefficient (TRC) of STAR-RIS, which is controlled by the CPU.

[0046] set up individual users and Located in the transmission (t) region, while individual users and Located in the reflection (r) region, where .Will , , , and Represented as from the first The base station to the Channels for individual users and eavesdroppers ( ), from the From one base station to STAR-RIS, from STAR-RIS to area( The first There are two types of users and eavesdroppers. Eavesdroppers are considered internal users and cannot be trusted by other users; therefore, channel estimation methods can be used to obtain legitimate / eavesdropping channel state information. Let... 1 and .

[0047] Unless otherwise stated, and .

[0048] Please see Figure 7 The present invention discloses a secure transmission method for maximizing weighted sum and confidentiality in a STAR-RIS-assisted cellless network, comprising the following steps: S1. Obtain all channel state information of the Cell-Free downlink network, and initialize the beamforming direction and STAR-RIS transmission and reflection coefficients of each base station. Each STAR-RIS component has three operating modes, each corresponding to a different operating protocol. The research focuses on the Energy Splitting (ES) protocol, which allows each component to operate simultaneously in both transmission and reflection modes. This is achieved by analyzing the amplitude coefficients of the reflection and transmission phase shifts (…). and The signal energy received by STAR-RIS is divided into two parts, of which... The main research direction is low-cost passive and lossless STAR-RIS, which is activated only when an incident signal is received, generating magnetizing and polarizing currents as well as corresponding transmission and reflection fields.

[0049] It is important to note that the total energy of the transmitted and reflected signals equals the energy of the incident signal, thus creating unavoidable coupling between the TRCs. Therefore, it can be obtained that...

[0050] The specific transmission / reflection coefficient matrix of STAR-RIS is as follows:

[0051] in, An index for a single element in STAR-RIS. This represents the total number of components in STAR-RIS. Let be the transmission energy distribution coefficient of the nth STAR-RIS element. Let n be the reflection energy distribution coefficient of the nth STAR-RIS element. Let n be the phase shift coefficient applied to the transmitted signal by the nth STAR-RIS element. Let n be the phase shift coefficient applied to the reflected signal by the nth STAR-RIS element. For the first A STAR-RIS component in The amplitude coefficient of the region, For the corresponding phase shift, the coupling constraint must be satisfied. and , For STAR-RIS The complex coefficient diagonal matrix of the region, For the nth STAR-RIS element in The amplitude modulation coefficient of the region, For the nth STAR-RIS element in The phase shift modulation factor of the region.

[0052] S2. Based on the channel state information obtained in step S1, construct the received signal models of the user and the eavesdropper, calculate the signal-to-interference-plus-noise ratio (SIR) of the user and the SIR of the eavesdropper intercepting the user's information, and establish an optimization problem with the goal of maximizing the weighted sum and the confidentiality rate. No. Each base station employs multiple beamforming vectors Confidential signals Securely transmit to Within the area There are [number] users. The power of the transmitted signal has been normalized, where [the power is] [normalized]. From the first The data transmitted by each base station is represented as follows: .

[0053] No. individual users and The signal received by the eavesdropper in the area is represented as:

[0054]

[0055] in, and This represents additive white Gaussian noise (AWGN). For the first The transmission signals of each base station To satisfy the normalized confidential signal $ and It is additive white Gaussian noise.

[0056] make , , and .

[0057] No. individual users and in Intercepted within the area The signal-to-interference-plus-noise ratio (SIR) of an eavesdropper on individual user information is as follows:

[0058]

[0059] in, , and These are the equivalent channel matrices.

[0060] S3. For the non-convex optimization problem established in step S2, the objective function is linearized using the continuous convex approximation method. Given the optimization variables of the iteration, the user information rate and the eavesdropper information rate are transformed into convex lower bound approximate expressions about the current iteration point through first-order Taylor expansion, thus obtaining an approximate problem that can be solved by convex optimization tools. This invention aims to design an effective communication strategy to maximize the WSSR of legitimate users. In this process, the limitations of transmit power and the complex relationship between amplitude and phase offset must be carefully considered. To overcome these challenges, a method is employed that jointly optimizes the transmit beamforming of the base station and the TRC of STAR-RIS. The problem is given by the following formula:

[0061] in, , Considered as satisfied The The weight of each user For the first The transmit power of each base station, for The total number of legitimate users in the region. For the sake of confidentiality, for The first in the region Channel phase factor for each user, for Eavesdroppers in the area intercepted the first The signal-to-interference-to-noise ratio of individual user information.

[0062] Given ,but The first in the region The information rate of an individual user is expressed as:

[0063] definition

[0064]

[0065]

[0066]

[0067]

[0068] Among them, superscript Indicates the first The value of the next iteration.

[0069] To approximate the eavesdropper's information rate using a linearized method, it is first rewritten as:

[0070] in

[0071] also, ,as well as .

[0072] Approximate conversion

[0073] and Approximately:

[0074] S4. Using the linearized objective function obtained in step S3, under the condition of fixed STAR-RIS transmission / reflection coefficients, the optimization problem is transformed into a convex optimization subproblem about the beamforming vectors of all base stations. The constraint condition only includes the transmit power limit of each base station. The updated beamforming vector is obtained by solving the problem using the CVX convex optimization toolbox and used in step S5. The beamforming optimization subproblem after fixing the STAR-RIS coefficients is as follows:

[0075] This problem is a convex optimization problem.

[0076] Solve this using the CVX toolkit.

[0077] By a fixed beamforming vector and given Rewrite the original question as about The new problem is detailed below.

[0078] here and The calculation method is the same as in the previous section, therefore , and In addition, there are

[0079] therefore, It can be approximated as

[0080] in .

[0081] For the sake of brevity, define ,therefore , Then define ,and .

[0082] set up , .definition ,make Therefore, regarding The subproblem can be simplified to

[0083] S5. Using the beamforming vector obtained in step S4, optimize the STAR-RIS transmission / reflection coefficients under the condition of fixed beamforming. Due to the existence of coupling phase shift constraints, auxiliary variables are introduced and the augmented Lagrangian method is used to transform the equality constraints into penalty terms in the objective function. Closed-form solutions for the amplitude coefficients and phase shift coefficients of STAR-RIS are obtained through alternating optimization. In the above problems, besides equality constraints, for and There are no restrictions. To address this constraint, the present invention employs a strategy that transforms it into a penalty term in the objective function. By implementing this transformation, the initial optimization problem can be converted into an Augmented Lagrangian (AL) problem, thereby improving the flexibility and efficiency of the solution.

[0084] The STAR-RIS optimization subproblem after fixed beamforming is transformed into the following using the augmented Lagrangian method:

[0085] in, ,in It is the penalty parameter for violating equality constraints. represents the Lagrange dual variable.

[0086] Will If we set it to zero, we get... Furthermore, the equality constraint is enforced.

[0087] Next, the optimization variables will be broken down into... and .

[0088] Regarding the subproblem It's important to note that even after introducing the penalty term, the problem remains convex. Therefore, the subproblems... It can be represented as

[0089] Therefore, this problem can be solved using the CVX toolbox.

[0090] For subproblems By extracting information about the variables from the objective function The terms can be used to solve the problem.

[0091] in, .Will Decomposed into and ,satisfy

[0092] therefore The problem was solved through further investigation. and The two subproblems are solved.

[0093] The problem is described as follows

[0094] here Representing constraints After removing irrelevant items, The problem is rewritten as

[0095] definition Then for any given and The closed-form solution for the phase shift coefficient is:

[0096] in, Represent a set of closed-form solutions.

[0097] definition , , , For any given optimal value and , and The closed-form solution for amplitude optimization is:

[0098] in

[0099] in, To augment the constraint penalty term of the Lagrange function, is the optimal phase shift coefficient for the nth STAR-RIS element.

[0100] S6. Substitute the STAR-RIS coefficients obtained in step S5 and the beamforming vector obtained in step S4 into the objective function of step S2 to calculate the weighted sum and security rate of the current iteration. Determine whether the convergence condition is met. If it is met, output the optimal beamforming vector and STAR-RIS coefficients. If it is not met, return to step S3 to continue the iteration.

[0101] In another embodiment of the present invention, a secure transmission system for maximizing weighted sum and confidentiality in a STAR-RIS-assisted cellless network is provided. This system can be used to implement the aforementioned secure transmission method for maximizing weighted sum and confidentiality in a STAR-RIS-assisted cellless network. Specifically, the secure transmission system for maximizing weighted sum and confidentiality in a STAR-RIS-assisted cellless network includes a channel information acquisition and initialization module, a receiving model construction module, a linearization processing module, a beamforming optimization module, a STAR-RIS coefficient optimization module, and a convergence judgment and output module.

[0102] The channel information acquisition and initialization module is used to acquire the channel state information of the STAR-RIS assisted Cell-Free downlink network and initialize the beamforming vector of each base station and the transmission / reflection coefficients of STAR-RIS; wherein the transmission / reflection coefficients include amplitude coefficients and phase shift coefficients, and satisfy the coupling constraints between transmission and reflection. The receiving model construction module is used to construct the received signal models of the user and the eavesdropper based on the channel state information, beamforming vector and STAR-RIS transmission / reflection coefficient, calculate the signal-to-interference-plus-noise ratio of the user and the signal-to-interference-plus-noise ratio of the eavesdropper intercepting the user's information, determine the user's confidentiality rate based on the difference between the user's information rate and the eavesdropper's information rate, and establish an optimization problem with the goal of maximizing the weighted sum confidentiality rate. The linearization module is used to linearize the objective function for the optimization problem using a continuous convex approximation method. Given the optimization variables of the current iteration, the user information rate and the eavesdropper information rate are transformed into convex lower bound approximate expressions about the current iteration point through a first-order Taylor expansion, thus obtaining an approximate problem that can be solved by convex optimization. The beamforming optimization module is used to transform the approximate problem into a convex optimization subproblem about the beamforming vector of each base station under the condition of fixed STAR-RIS transmission / reflection coefficients by using the linearized objective function obtained by the linearization processing module, and to solve the updated beamforming vector under the transmit power limit of each base station. The STAR-RIS coefficient optimization module is used to optimize the STAR-RIS transmission / reflection coefficients under the condition of a fixed beamforming vector using the beamforming optimization module. For the coupling constraints of the STAR-RIS transmission / reflection coefficients, auxiliary variables are introduced and the augmented Lagrangian method is used to transform the equality constraints into penalty terms in the objective function. The amplitude coefficients and phase shift coefficients of STAR-RIS are updated by alternately optimizing. The convergence judgment and output module is used to substitute the updated STAR-RIS transmission / reflection coefficients and the updated beamforming vector into the objective function of the optimization problem, calculate the weighted sum and security rate of the current iteration, and determine whether the convergence condition is met. If it is met, the optimized beamforming vector and STAR-RIS transmission / reflection coefficients are output. If it is not met, the linearization processing module is triggered to continue the iteration.

[0103] This invention provides a terminal device comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve a corresponding method flow or function. The processor described in this embodiment can be used in the operation of a secure transmission method that maximizes weighting and confidentiality in a STAR-RIS-assisted cellless network, including: S1. Obtain the channel state information of the STAR-RIS assisted Cell-Free downlink network, and initialize the beamforming vector of each base station and the transmission / reflection coefficients of STAR-RIS; wherein, the transmission / reflection coefficients include amplitude coefficients and phase shift coefficients, and satisfy the coupling constraints between transmission and reflection; S2. Based on the channel state information, beamforming vector and STAR-RIS transmission / reflection coefficients obtained in step S1, construct the received signal models of the user and the eavesdropper, calculate the signal-to-interference-plus-noise ratio of the user and the signal-to-interference-plus-noise ratio of the eavesdropper intercepting the user's information, and determine the user's confidentiality rate based on the difference between the user's information rate and the eavesdropper's information rate, and establish an optimization problem with the goal of maximizing the weighted sum confidentiality rate. S3. For the optimization problem established in step S2, the objective function is linearized using the continuous convex approximation method. Given the optimization variables of the current iteration, the user information rate and the eavesdropper information rate are transformed into convex lower bound approximate expressions about the current iteration point through first-order Taylor expansion, thus obtaining an approximate problem that can be solved by convex optimization. S4. Using the linearized objective function obtained in step S3, under the condition of fixed STAR-RIS transmission / reflection coefficients, the approximate problem is transformed into a convex optimization subproblem about the beamforming vector of each base station, and the updated beamforming vector is obtained by solving it under the transmit power limit of each base station. S5. Using the beamforming vector obtained in step S4, optimize the STAR-RIS transmission / reflection coefficients under the condition of fixed beamforming vectors. For the coupling constraints of the STAR-RIS transmission / reflection coefficients, introduce auxiliary variables and use the augmented Lagrangian method to transform the equality constraints into penalty terms in the objective function. Update the amplitude coefficients and phase shift coefficients of STAR-RIS by alternate optimization. S6. Substitute the updated STAR-RIS transmission / reflection coefficients from step S5 and the updated beamforming vector from step S4 into the objective function of step S2, calculate the weighted sum and security rate of the current iteration, and determine whether the convergence condition is met. If it is met, output the optimized beamforming vector and STAR-RIS transmission / reflection coefficients. If it is not met, return to step S3 to continue the iteration.

[0104] Please see Figure 5 The terminal device is a computer device. In this embodiment, the computer device 60 includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When executed by the processor 61, the computer program 63 implements the secure transmission method for maximizing weighted and confidentiality rates in the STAR-RIS-assisted cellless network of this embodiment. To avoid repetition, these details are not elaborated here. Alternatively, when executed by the processor 61, the computer program 63 implements the functions of each model / unit in the secure transmission system for maximizing weighted and confidentiality rates in the STAR-RIS-assisted cellless network of this embodiment. To avoid repetition, these details are not elaborated here.

[0105] Computer device 60 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will understand that... Figure 5 This is merely an example of computer device 60 and does not constitute a limitation on computer device 60. It may include more or fewer components than shown, or combine certain components, or different components. For example, computer device may also include input / output devices, network access devices, buses, etc.

[0106] The processor 61 may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0107] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on the computer device 60.

[0108] Furthermore, the memory 62 may include both internal storage units of the computer device 60 and external storage devices. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0109] Please see Figure 6 The terminal device is an electronic device 600, which is manifested in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including storage unit 620 and processing unit 610), a display unit 640, etc.

[0110] The storage unit stores program code, which can be executed by the processing unit 610 to perform the steps described in the method section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 610 can perform actions such as... Figure 7 The steps are shown in the figure.

[0111] Storage unit 620 may include readable media in the form of volatile storage units, such as random access memory (RAM) 6201 and / or cache memory 6202, and may further include read-only memory (ROM) 6203.

[0112] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0113] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.

[0114] Electronic device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem). This communication can be performed via input / output interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network, wide area network, and / or public network, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0115] Example 4 This invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It is understood that the computer-readable storage medium here can include both built-in storage media in the terminal device and extended storage media supported by the terminal device; it can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor, which can be one or more computer programs (including program code). More specific examples of the computer-readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical fiber, portable compact disk read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0116] Computer-readable storage media also include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium can also be any readable medium other than a readable storage medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, radio frequency, etc., or any suitable combination thereof.

[0117] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0118] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the secure transmission method for maximizing weighted and confidentiality rates in a STAR-RIS-assisted cellless network as described in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor to perform the following steps: S1. Obtain the channel state information of the STAR-RIS assisted Cell-Free downlink network, and initialize the beamforming vector of each base station and the transmission / reflection coefficients of STAR-RIS; wherein, the transmission / reflection coefficients include amplitude coefficients and phase shift coefficients, and satisfy the coupling constraints between transmission and reflection; S2. Based on the channel state information, beamforming vector and STAR-RIS transmission / reflection coefficients obtained in step S1, construct the received signal models of the user and the eavesdropper, calculate the signal-to-interference-plus-noise ratio of the user and the signal-to-interference-plus-noise ratio of the eavesdropper intercepting the user's information, and determine the user's confidentiality rate based on the difference between the user's information rate and the eavesdropper's information rate, and establish an optimization problem with the goal of maximizing the weighted sum confidentiality rate. S3. For the optimization problem established in step S2, the objective function is linearized using the continuous convex approximation method. Given the optimization variables of the current iteration, the user information rate and the eavesdropper information rate are transformed into convex lower bound approximate expressions about the current iteration point through first-order Taylor expansion, thus obtaining an approximate problem that can be solved by convex optimization. S4. Using the linearized objective function obtained in step S3, under the condition of fixed STAR-RIS transmission / reflection coefficients, the approximate problem is transformed into a convex optimization subproblem about the beamforming vector of each base station, and the updated beamforming vector is obtained by solving it under the transmit power limit of each base station. S5. Using the beamforming vector obtained in step S4, optimize the STAR-RIS transmission / reflection coefficients under the condition of fixed beamforming vectors. For the coupling constraints of the STAR-RIS transmission / reflection coefficients, introduce auxiliary variables and use the augmented Lagrangian method to transform the equality constraints into penalty terms in the objective function. Update the amplitude coefficients and phase shift coefficients of STAR-RIS by alternate optimization. S6. Substitute the updated STAR-RIS transmission / reflection coefficients from step S5 and the updated beamforming vector from step S4 into the objective function of step S2, calculate the weighted sum and security rate of the current iteration, and determine whether the convergence condition is met. If it is met, output the optimized beamforming vector and STAR-RIS transmission / reflection coefficients. If it is not met, return to step S3 to continue the iteration.

[0119] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0120] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0121] The following is a description of the simulation environment; the simulation parameters used are as follows: , , and dBm. Noise power is dBm. Assume the distance between the base station and STAR-RIS is 50 meters. Users are distributed in a circle with a radius of 5 meters centered on STAR-RIS. The reflection area has... Number of users, transmission area has There are [number] users. Furthermore, for each legitimate user, a potential eavesdropper is randomly placed within a circular area with a radius of 2 meters centered on the nearest user. Additionally, The channel is modeled as a Ricean fading channel with a Ricean factor of 3 dB and a path loss exponent of 2.2. At a reference distance of 1 meter, the path loss is set to -30 dB. The average value is calculated from 200 channels at each point.

[0122] To comprehensively evaluate the performance advantages of the proposed algorithm, this invention compares it with the following benchmark schemes: STAR-RIS, Independent: The idealized assumption that the phase shift of each STAR-RIS cell can be optimized independently can be used as an upper bound for performance. STAR-RIS, Coupled discrete phase: The proposed algorithm optimizes the phase shift and discretizes it. STAR-RIS, Random phase: Phase shift parameters are set randomly without optimization; Conventional RIS: Deploying conventional RIS with either only reflection or only transmission in the same location; NoRIS: A baseline scenario without deploying any RIS auxiliary equipment.

[0123] Please see Figure 2 The figure illustrates the convergence characteristics of the algorithm under different values ​​of N and M. Simulation results show that the WSSR converges rapidly to a stable value during the iteration process, which fully verifies the effectiveness of the proposed algorithm. Notably, increasing the number of STAR-RIS components can significantly improve the WSSR, providing an important reference for practical system design.

[0124] Please see Figure 3 This figure illustrates the impact of varying distances between the base station and STAR-RIS on the WSSR performance of each scheme. Comparison of the curves reveals that the proposed coupled phase-shift control scheme exhibits performance levels similar to the ideal independent phase-shift control scheme and significantly outperforms the traditional RIS scheme. This performance advantage is particularly pronounced when the base station and STAR-RIS are close together. With increasing distance, the cumulative effect of channel propagation loss leads to a decrease in WSSR for all schemes. This observation highlights the importance of deploying STAR-RIS near the base station for achieving highly reliable and secure transmission.

[0125] Please see Figure 4 The figure compares and analyzes the security performance of different schemes under varying base station transmit power. Observing the simulation curves, it can be seen that although the performance of the coupled phase-shift control scheme is slightly inferior to the ideal independent phase-shift control scheme, it still achieves a significant performance gain compared to the traditional RIS scheme. This performance difference mainly stems from the limitation of optimization degrees of freedom imposed by the coupling constraint. Another noteworthy phenomenon is that as the transmit power increases, although the WSSR of each scheme continues to grow, the rate of increase gradually slows down. This is because co-channel interference becomes more prominent in the high-power region. Regarding the impact of phase-shift discretization, although it does cause some performance loss, the proposed scheme still maintains a significant advantage over the random phase-shift scheme and the non-RIS scheme. The reason for this is that the random phase-shift scheme lacks targeted beamforming capabilities, failing to effectively enhance the desired signal or suppress interference from the eavesdropping channel.

[0126] Based on the above analysis, simulation results show that the performance of the proposed method is close to that of an ideal independent phase-shift scheme, achieving a 32% improvement compared to the traditional RIS scheme and a 65% improvement compared to a RIS-free scheme. The algorithm converges quickly, achieving stable convergence in an average of 15 iterations, significantly reducing computational complexity and making it suitable for real-time deployment in large-scale cell-free networks. Furthermore, this method can simultaneously serve users on both sides of the STAR-RIS surface, expanding network coverage and improving spectral efficiency. In a typical scenario with a base station and STAR-RIS distance of 50 meters and a transmit power of 20 dBm, the system weighted and security rate can reach 18.5 bits / s / Hz, meeting the high-security and high-capacity transmission requirements of future 6G communication systems.

[0127] In summary, this invention presents a secure transmission method for maximizing weighted sum and security rate in a STAR-RIS-assisted cell-free network. Addressing the coupled phase shift characteristics of practical STAR-RIS systems, it establishes a weighted sum and security rate maximization problem considering coupling constraints. A continuous convex approximation method is used to linearize the non-convex objective function. Then, an alternating optimization strategy is employed to optimize the base station beamforming vector and the transmission / reflection coefficients of the STAR-RIS system. An augmented Lagrangian method is introduced to transform the coupled phase shift constraint into a penalty term, and closed-form solutions for the STAR-RIS amplitude and phase coefficients are derived. This not only effectively solves the optimization problem caused by the coupled phase shift constraint and significantly reduces the algorithm complexity, but also, compared to traditional RIS-assisted cell-free systems, enables services to users on both sides of the network, greatly improving the system's secure transmission performance and spectral efficiency.

[0128] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0129] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0130] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0131] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0132] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0133] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0134] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random-access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0135] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus, and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0136] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0137] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0138] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A secure transmission method for maximizing weighted sum and confidentiality in a STAR-RIS-assisted cellless network, characterized in that, Includes the following steps: S1. Obtain the channel state information of the STAR-RIS assisted Cell-Free downlink network, and initialize the beamforming vector of each base station and the transmission / reflection coefficients of STAR-RIS; wherein, the transmission / reflection coefficients include amplitude coefficients and phase shift coefficients, and satisfy the coupling constraints between transmission and reflection; S2. Based on the channel state information, beamforming vector and STAR-RIS transmission / reflection coefficients obtained in step S1, construct the received signal models of the user and the eavesdropper, calculate the signal-to-interference-plus-noise ratio of the user and the signal-to-interference-plus-noise ratio of the eavesdropper intercepting the user's information, and determine the user's confidentiality rate based on the difference between the user's information rate and the eavesdropper's information rate, and establish an optimization problem with the goal of maximizing the weighted sum confidentiality rate. S3. For the optimization problem established in step S2, the objective function is linearized using the continuous convex approximation method. Given the optimization variables of the current iteration, the user information rate and the eavesdropper information rate are transformed into convex lower bound approximate expressions about the current iteration point through first-order Taylor expansion, thus obtaining an approximate problem that can be solved by convex optimization. S4. Using the linearized objective function obtained in step S3, under the condition of fixed STAR-RIS transmission / reflection coefficients, the approximate problem is transformed into a convex optimization subproblem about the beamforming vector of each base station, and the updated beamforming vector is obtained by solving it under the transmit power limit of each base station. S5. Using the beamforming vector obtained in step S4, optimize the STAR-RIS transmission / reflection coefficients under the condition of fixed beamforming vectors. For the coupling constraints of the STAR-RIS transmission / reflection coefficients, introduce auxiliary variables and use the augmented Lagrangian method to transform the equality constraints into penalty terms in the objective function. Update the amplitude coefficients and phase shift coefficients of STAR-RIS by alternate optimization. S6. Substitute the updated STAR-RIS transmission / reflection coefficients from step S5 and the updated beamforming vector from step S4 into the objective function of step S2, calculate the weighted sum and security rate of the current iteration, and determine whether the convergence condition is met. If it is met, output the optimized beamforming vector and STAR-RIS transmission / reflection coefficients. If it is not met, return to step S3 to continue the iteration.

2. The secure transmission method for maximizing weighted sum and confidentiality rate in a STAR-RIS-assisted cellless network according to claim 1, characterized in that, In step S1, the channel state information includes: Channel state information from each base station to each user, channel state information from each base station to each eavesdropper, channel state information from each base station to STAR-RIS, channel state information from STAR-RIS to each user, and channel state information from STAR-RIS to each eavesdropper. The transmission / reflection coefficients of STAR-RIS include transmission amplitude coefficient, reflection amplitude coefficient, transmission phase shift coefficient, and reflection phase shift coefficient.

3. The secure transmission method for maximizing weighted sum and confidentiality rate in a STAR-RIS-assisted cellless network according to claim 2, characterized in that, The transmission amplitude coefficient and the reflection amplitude coefficient satisfy the energy splitting constraint, and the transmission phase shift coefficient and the reflection phase shift coefficient satisfy the phase coupling constraint; The energy splitting constraint is used to characterize the distribution relationship of incident signal energy between the transmission and reflection directions of the STAR-RIS unit, and the phase coupling constraint is used to characterize the correlation relationship between the transmission phase and the reflection phase of the same STAR-RIS unit.

4. The secure transmission method for maximizing weighted sum and confidentiality rate in a STAR-RIS-assisted cellless network according to claim 1, characterized in that, In step S2, when constructing the received signal models of users and eavesdroppers, the received signals of users and eavesdroppers are determined according to the confidential signals sent by each base station, the beamforming vectors of each base station, the direct link channels, and the cascaded link channels formed by STAR-RIS. Based on the expected signal power, interference signal power, and noise power in the user's received signal and the eavesdropper's received signal, the signal-to-interference-plus-noise ratio (SIR) of the user and the SIR of the eavesdropper intercepting the user's information are calculated respectively.

5. The secure transmission method for maximizing weighted sum and confidentiality in a STAR-RIS-assisted cellless network according to claim 1, characterized in that, In step S2, the user information rate is determined based on the user's signal-to-interference-plus-noise ratio (SIR), the eavesdropper information rate is determined based on the SIR of the user's information intercepted by the eavesdropper, and the difference between the user information rate and the eavesdropper information rate is used as the user's confidentiality rate. Based on the weights corresponding to each user, the confidentiality rates of each user are summed in a weighted manner to obtain the weighted sum confidentiality rate, and the optimization problem is established with the goal of maximizing the weighted sum confidentiality rate.

6. The secure transmission method for maximizing weighted sum and confidentiality rate in a STAR-RIS-assisted cellless network according to claim 1, characterized in that, In step S3, when linearizing the user information rate and the eavesdropper information rate, the optimization variable at the current iteration point is used as the expansion point, and the non-convex terms in the user information rate and the eavesdropper information rate are expanded by first-order Taylor expansion to obtain an approximate expression for the convex lower bound of the current iteration point. The non-convex objective function term in the original optimization problem is replaced with the convex lower bound approximation expression to obtain the approximate problem.

7. The secure transmission method for maximizing weighted sum and confidentiality rate in a STAR-RIS-assisted cellless network according to claim 1, characterized in that, In step S4, under the condition of fixed STAR-RIS transmission / reflection coefficients, the equivalent channels of users and eavesdroppers are determined as fixed parameters, and the approximation problem is transformed into a convex optimization subproblem with the beamforming vectors of each base station as optimization variables. in, It is the set of beamforming vectors for all base stations. for Number of users in the region For regional identification, Index for users, For information rate, For the first In the next iteration Region 1 The weight of each user for Region 1 The weight of each user For the first The coefficients of the first-order term after linearizing the user information rate in the next iteration. The coefficients of the first-order term after linearizing the user information rate. For interference and noise terms in the eavesdropper's information rate, For the first The approximate term after linearizing the eavesdropper's information rate in the next iteration. The conjugate transpose of the beamforming matrix. for Region 1 Equivalent channel matrix for each user This is the conjugate transpose of the equivalent channel matrix. For the first Beamforming matrix of the next iteration For the first The maximum transmit power of each base station For base station indexing, The total number of base stations. For the first base stations Region 1 Beamforming vectors for each user; The constraints of the convex optimization subproblem include at least the transmit power limit of each base station. The updated beamforming vector of each base station is obtained by solving the convex optimization subproblem.

8. The secure transmission method for maximizing weighted sum and confidentiality rate in a STAR-RIS-assisted cellless network according to claim 1, characterized in that, In step S5, under the condition of fixed beamforming vector, the optimization problem of the STAR-RIS transmission / reflection coefficient is transformed into a STAR-RIS coefficient optimization subproblem with STAR-RIS amplitude coefficient and phase shift coefficient as optimization variables; To address the coupling constraints of the STAR-RIS transmission / reflection coefficients, auxiliary variables and Lagrange dual variables are introduced to construct an augmented Lagrange function, and the equality constraints corresponding to the coupling constraints are transformed into penalty terms in the augmented Lagrange function.

9. The secure transmission method for maximizing weighted sum and confidentiality rate in a STAR-RIS-assisted cellless network according to claim 8, characterized in that, When performing alternating optimization on the STAR-RIS coefficient optimization subproblem, the STAR-RIS coefficient optimization subproblem is decomposed into a phase shift coefficient optimization subproblem and an amplitude coefficient optimization subproblem; Given the amplitude coefficient, solve the phase shift coefficient optimization subproblem to obtain the updated phase shift coefficient; Given the updated phase shift coefficients, solve the amplitude coefficient optimization subproblem to obtain the updated amplitude coefficients; The updated STAR-RIS transmission / reflection coefficients are determined based on the updated phase shift coefficients and the updated amplitude coefficients.

10. A secure transmission system for maximizing weighted sum and confidentiality in a STAR-RIS-assisted cellless network, characterized in that, include: The channel information acquisition and initialization module is used to acquire the channel state information of the STAR-RIS assisted Cell-Free downlink network and initialize the beamforming vector of each base station and the transmission / reflection coefficients of STAR-RIS; wherein the transmission / reflection coefficients include amplitude coefficients and phase shift coefficients, and satisfy the coupling constraints between transmission and reflection; The receiving model construction module is used to construct the received signal models of the user and the eavesdropper based on the channel state information, beamforming vector and STAR-RIS transmission / reflection coefficient, calculate the signal-to-interference-plus-noise ratio of the user and the signal-to-interference-plus-noise ratio of the eavesdropper intercepting the user's information, determine the user's confidentiality rate based on the difference between the user's information rate and the eavesdropper's information rate, and establish an optimization problem with the goal of maximizing the weighted sum confidentiality rate. The linearization module is used to linearize the objective function for the optimization problem using a continuous convex approximation method. Given the optimization variables of the current iteration, the user information rate and the eavesdropper information rate are transformed into convex lower bound approximate expressions about the current iteration point through a first-order Taylor expansion, thus obtaining an approximate problem that can be solved by convex optimization. The beamforming optimization module is used to transform the approximate problem into a convex optimization subproblem about the beamforming vector of each base station under the condition of fixed STAR-RIS transmission / reflection coefficients by using the linearized objective function obtained by the linearization processing module, and to solve the updated beamforming vector under the transmit power limit of each base station. The STAR-RIS coefficient optimization module is used to optimize the STAR-RIS transmission / reflection coefficients under the condition of a fixed beamforming vector using the beamforming optimization module. For the coupling constraints of the STAR-RIS transmission / reflection coefficients, auxiliary variables are introduced and the augmented Lagrangian method is used to transform the equality constraints into penalty terms in the objective function. The amplitude coefficients and phase shift coefficients of STAR-RIS are updated by alternately optimizing. The convergence judgment and output module is used to substitute the updated STAR-RIS transmission / reflection coefficients and the updated beamforming vector into the objective function of the optimization problem, calculate the weighted sum and security rate of the current iteration, and determine whether the convergence condition is met. If it is met, the optimized beamforming vector and STAR-RIS transmission / reflection coefficients are output. If it is not met, the linearization processing module is triggered to continue the iteration.

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