Security communication method for intelligent reflecting surface assisted MISO-RSMA communication system

Abstract

The present application relates to the technical field of wireless communication security, and specifically relates to a security communication method for an intelligent reflecting surface assisted MISO-RSMA communication system, which comprises: establishing an intelligent reflecting surface assisted multi-user MISO-RSMA communication system; designing a beamforming vector and a phase shift vector to maximize an optimization problem of a security rate; converting the optimization problem into a partial variable decoupling optimization problem by introducing a relaxation variable; dividing the partial variable decoupling optimization problem into a beamforming vector sub-optimization problem and a phase shift vector sub-optimization problem; alternately iterating and optimizing the beamforming vector sub-optimization problem and the phase shift vector sub-optimization problem until convergence, and outputting optimal beamforming vectors and phase shift vectors to realize security communication. The present application can be expanded from a two-user communication system to a more general multi-user scenario, and can combine intelligent reflecting surfaces to improve the security performance of the system.

Description

Technical Field

[0001] This invention relates to the field of wireless communication security technology, and more specifically to a secure communication method for intelligent reflector-assisted MISO-RSMA communication systems. Background Technology

[0002] The broadcast nature of wireless signals creates opportunities for unauthorized users to eavesdrop on private information, posing a security threat to wireless communication. To address this security issue, physical layer security leverages the reciprocity, uniqueness, and time-varying nature of wireless channels. Utilizing communication transmission techniques such as coding and modulation, it ensures the communication quality for legitimate users while reducing the received signal quality for eavesdroppers, thus achieving secure wireless signal transmission.

[0003] Physical layer security can be traced back to the information theory encryption proposed by Shannon in 1949. In the decades that followed, physical layer security made significant progress, with beamforming and artificial noise being two typical techniques. However, as wireless network environments become increasingly complex, for example, when an unauthorized user's location is close to that of a legitimate user, their channels become correlated, making beamforming and artificial noise insufficient to effectively guarantee wireless communication security. Yet, physical layer security based on reflecting intelligent surfaces (RIS) offers a new solution. RIS possesses characteristics such as low power consumption, low cost, and reconfigurability. By intelligently controlling an "artificial wireless channel," it improves the performance of legitimate communication and reduces the received signal quality for unauthorized users, thereby ensuring wireless communication security.

[0004] However, eavesdroppers are covert nodes that only receive signals, making it difficult to obtain accurate Channel State Information (CSI), and obtaining CSI from smart reflectors remains a significant challenge. When the transmitter's Channel State Information (CSIT) is complete, beamforming and artificial noise cannot be precisely targeted at the user, reducing security gain. Besides employing robust designs to handle imperfect CSIT, the recently emerging RSMA (Rate Splitting Multiple Access) scheme possesses inherent robustness against incomplete CSIT. RSMA is a RS-based multiple access method that divides user messages into public and private parts, partially decodes interference, and treats the remaining interference as noise. In effect, it is a general framework that softly connects NOMA and Space Division Multiple Access (SDMA).

[0005] Multiple-input single-output rate-division multiple access (MISO-RSMA) communication systems and their physical layer security have attracted widespread attention. However, existing research mainly focuses on two-user communication systems, neglecting more general multi-user scenarios. Furthermore, current research completely ignores the use of Intelligent Reflection (RIS) to enhance system security, resulting in insufficient communication security and robustness. Therefore, designing a communication scheme that improves the communication security and robustness of MISO-RSMA systems is a pressing technical problem that needs to be solved. Summary of the Invention

[0006] To address the shortcomings of the existing technologies, the technical problem to be solved by this invention is: how to provide a secure communication method for intelligent reflective surface-assisted MISO-RSMA communication systems, which can be extended from two-user communication systems to more general multi-user scenarios, and can be combined with intelligent reflective surfaces to improve the security performance of the system, thereby improving the communication security and robustness of multi-user MISO-RSMA communication systems.

[0007] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0008] A secure communication method for intelligent reflector-assisted MISO-RSMA communication systems includes:

[0009] S1: Establish a multi-user MISO-RSMA communication system assisted by an intelligent reflective surface;

[0010] S2: For multi-user MISO-RSMA communication systems, establish an optimization problem to maximize the secure rate by designing beamforming vectors and phase shift vectors;

[0011] S3: By introducing slack variables, the optimization problem is transformed into an optimization problem with partial variable decoupling;

[0012] S4: The optimization problem with partial variable decoupling is divided into beamforming vector quantum optimization problem and phase shift vector quantum optimization problem;

[0013] S5: Iteratively optimize the beamforming vector quantum optimization problem and the phase shift vector quantum optimization problem until convergence, output the optimal beamforming vector and phase shift vector, and design the beamforming vector in the base station and the phase shift vector in the smart reflector to achieve secure communication.

[0014] Preferably, the multi-user MISO-RSMA communication system performs rate segmentation on the user signal transmission and reception process.

[0015] Preferably, the multi-user MISO-RSMA communication system includes an N-antenna base station, a smart reflector, K single-antenna legitimate users, and 1 single-antenna eavesdropping party;

[0016] The intelligent reflective surface has M reflective units, each of which reflects wave signals by adjusting the phase reflection;

[0017] In the RSMA downlink transmission process of a multi-user MISO-RSMA communication system, the k to be sent is first... th User source information W k Information divided into common parts W c,k And private information W p,k Secondly, all users' public information is merged into a single public information W. c After modulation, a common signal s is obtained. c ; Each user's private information W p,k The private signal s is obtained by modulation respectively. k Finally, the public signal and all users' private signals are linearly precoded and transmitted respectively.

[0018] At the receiving end, after each user receives the signal, the common signal is first decoded and segmented to obtain their respective common part information. Then, serial interference cancellation technology is used to remove common signals and decode private information. In turn, complete user information source information can be obtained.

[0019] Preferably, the secure rate is used as the security performance indicator for a multi-user MISO-RSMA communication system. The secure rate is maximized by designing beamforming vectors and phase shift vectors, and the optimization problem of maximizing the secure rate is expressed by the following formula (P1):

[0020]

[0021] In the formula: ω represents the achievable safe rate for user k; c ω k ω z They represent public information s respectively c ,k user's private signal s k Beamforming vector of artificial noise z; v m C1 represents the phase shift vector of the m-th intelligent reflector unit; C1 represents the total power constraint of the base station, whose maximum total power is P. S C2 represents the phase shift constraint of the smart reflector;

[0022] in:

[0023]

[0024] R c,k =α k log2(1+SINR c,k );

[0025]

[0026] R cE,k =α k log2(1+SINR cE );

[0027]

[0028] R p,k =log2(1+SINR) p,k );

[0029]

[0030] R pE,k =log2(1+SINR) pE,k );

[0031]

[0032] Where: h B,k This represents the direct channel from the base station to the k-th user; v H H represents the conjugate transpose of the phase shift vector; B,k σ represents the joint channel from the base station to the k-th user; 2 R represents the variance of the background noise. c,k R p,k R represents the reachable rate of user k with respect to public and private signals; cE,k R pE,k SINR represents the rate of eavesdropping on user k's public and private signals. c,k SINR p,k SINR represents the received signal-to-noise ratio (SNR) of user k's public and private signals. cE SINR pE,k α represents the eavesdropping rate of user k's public and private signals; k This represents the proportion of the signal that user k occupies in the public signal.

[0033] Preferably, firstly, slack variables are introduced into the optimization problem (P1). Transform (P1) into an optimization problem with partial variable decoupling (P2):

[0034]

[0035] in: μ c , μ p,k These are introduced slack variables; the optimal solution of optimization problem (P2) is equivalent to that of optimization problem (P1);

[0036] Secondly, slack variables t and r are introduced into the optimization problem of partial variable decoupling (P2). c,k r c1,k r p,k t c,k β cE t cE t p,k β pE,k and t pE The optimization problem of generating partial variable decoupling (P3):

[0037]

[0038] Where: t, r c,k r c1,k r p,k t c,k β cE t cE t p,k β pE,k and t pE These are the introduced slack variables; the optimal solution of optimization problem (P3) is equivalent to the optimal solution of (P2); the objective function in optimization problem (P2) is equivalent to the objective function in optimization problem (P3) and C7a, C7b, C7c, C7d; C3 is equivalent to C3a and C3b, C4 is equivalent to C4a, C4b, and C4c, C5 is equivalent to C5a and C5b, and C6 is equivalent to C6a, C6b, and C6c.

[0039] Preferably, the optimization problem (P3) with some variables decoupled is divided into beamforming vector quantum optimization problem and phase shift vector quantum optimization problem;

[0040] Given the phase shift vector v, designing a robust beamforming vector yields the following beamforming vector sub-optimization problem (P4):

[0041]

[0042] When the beamforming vector ω c ω k and ω z Given that the optimal reflection coefficient vector is designed, the following phase shift vector sub-optimization problem is obtained (P5):

[0043]

[0044] Preferably, the infinite number of constraints and non-convex constraints of the beamforming vector quantum optimization problem (P4) and the phase shift vector quantum optimization problem (P5) are respectively approximated by convex approximation, and the equivalent transformation is obtained to obtain the sub-optimization problem (P5). ω ) and sub-optimization problems (P) v ).

[0045] Preferably, the sub-optimization problem (P) is expressed by the following formula: ω ):

[0046]

[0047] In the formula: It is obtained by approximating the infinite number of constraints and non-convex constraints C3a-C7d; u 4ah u 4aH u 4bh u 4bH u 6ah,k u 6aH,k u 6bh u 6bH This indicates the introduced variable.

[0048] Preferably, the sub-optimization problem (P) is expressed by the following formula: v ):

[0049]

[0050] In the formula: It is obtained by approximating the infinite number of constraints and non-convex constraints C3a-C7d; u 4ah u 4aH u 4bh u 4bH u 6ah,k u 6aH,k u 6bh u 6bH This indicates the introduced variable.

[0051] Preferably, secure communication is achieved through the following steps:

[0052] S501: Set n=0, cutoff parameter ε, and maximum number of loops, and initialize the randomized phase shift vector v. (0) ;

[0053] S502: For a given phase shift vector v (n) Solve the sub-optimization problem (P) ω To obtain the optimal beamforming vector.

[0054] S503: For a given beamforming vector Solving the sub-optimization problem (P) v To obtain the optimal phase shift vector v * and target value t (n) =t * ;

[0055] S504: Execute n = n + 1, let v (n) =v * ;

[0056] S505: If If the maximum number of loops is reached, proceed to step S506; otherwise, proceed to step S502.

[0057] S506: Output the optimal phase shift vector v * and beamforming vector And correspondingly, in the base station design beamforming vector Designing phase shift vector v in intelligent reflective surfaces * To achieve secure communication.

[0058] Compared with existing technologies, the secure communication method for intelligent reflector-assisted MISO-RSMA communication systems in this invention has the following advantages:

[0059] The multi-user MISO-RSMA communication system of the present invention can be extended from a two-user communication system to a more general multi-user scenario, and can be combined with a smart reflective surface (RIS) to improve the security performance of the system, thereby improving the communication security and versatility of the multi-user MISO-RSMA communication system.

[0060] This invention designs beamforming vectors and phase shift vectors for multi-user MISO-RSMA communication systems to maximize security rate. This allows for worst-case confidentiality by jointly optimizing the beamforming vectors of public messages, artificial noise, and each user's private messages, as well as the phase shift vector at the RIS. Furthermore, it enhances the system's resistance to channel uncertainties (performance advantages come from the simultaneous transmission of public and private messages and the use of artificial noise to reduce eavesdropping performance), thereby improving the communication security and robustness of multi-user MISO-RSMA communication systems.

[0061] This invention describes the robust design of secure beamforming vectors and phase shift vectors as a non-convex problem. The optimization problem aims to maximize the minimum security rate achievable in the worst case, but this optimization problem is difficult to solve. Therefore, this invention first introduces slack variables to transform the optimization problem into a tractable form with an equivalent objective function, i.e., a partially decoupled optimization problem. Then, the optimization problem is divided into beamforming vector sub-optimization problems and phase shift vector sub-optimization problems. Finally, the two sub-optimization problems are iteratively optimized alternately until convergence, and the optimal beamforming vector and phase shift vector are output to achieve secure communication. This allows for a fast and effective solution to the optimization problem of maximizing the secure rate of the system, thereby further improving the robustness and communication security of multi-user MISO-RSMA communication systems. Attached Figure Description

[0062] To make the objectives, technical solutions, and advantages of the invention clearer, the invention will now be described in further detail with reference to the accompanying drawings, wherein:

[0063] Figure 1 A logic block diagram of a secure communication method for a smart reflector-assisted MISO-RSMA communication system;

[0064] Figure 2 Architecture diagram of a multi-user MISO-RSMA communication system assisted by intelligent reflective surfaces;

[0065] Figure 3 This is a schematic diagram illustrating the change in safe rate with transmit power under imperfect CSI conditions.

[0066] Figure 4 This is a diagram illustrating how the safe rate varies with the number of legitimate receivers.

[0067] Figure 5 This is a schematic diagram illustrating how signal transmission power allocation varies with the number of users. Detailed Implementation

[0068] 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 only 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.

[0069] It should be noted that similar reference numerals and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the figures, or the orientation or positional relationship commonly used when the product is in use. They are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third," etc., are only used to distinguish descriptions and should not be construed as indicating or implying relative importance. In addition, the terms "horizontal," "vertical," etc., do not mean that the component is required to be absolutely horizontal or suspended, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but can be slightly tilted. In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0070] The following detailed explanation illustrates the specific implementation methods:

[0071] Example:

[0072] This embodiment discloses a secure communication method for a smart reflector-assisted MISO-RSMA communication system.

[0073] like Figure 1 As shown, a secure communication method for a smart reflector-assisted MISO-RSMA communication system includes:

[0074] S1: Establish a multi-user MISO-RSMA communication system assisted by an intelligent reflective surface;

[0075] S2: For a multi-user MISO-RSMA communication system, design beamforming vectors and phase shift vectors to maximize the safe rate optimization problem;

[0076] S3: By introducing slack variables, the optimization problem is transformed into an optimization problem with partial variable decoupling;

[0077] S4: The optimization problem with partial variable decoupling is divided into beamforming vector quantum optimization problem and phase shift vector quantum optimization problem;

[0078] In this embodiment, techniques such as continuous convex estimation, S-process, and GSD theorem are introduced to solve the two sub-problems.

[0079] S5: Iteratively optimize the beamforming vector quantum optimization problem and the phase shift vector quantum optimization problem until convergence, output the optimal beamforming vector and phase shift vector, and design the beamforming vector at the base station and the phase shift vector at the smart reflector to achieve secure communication.

[0080] This invention addresses the security risks posed by eavesdropping users to Multiple-Input Single-Output Rate Division Multiple Access (MISO-RSMA) communication systems. It establishes a smart reflector-assisted multi-user MISO-RSMA communication system and optimizes beamforming and phase-shift vectors to maximize the secure rate. On one hand, this invention's multi-user MISO-RSMA communication system can be extended from two-user systems to more general multi-user scenarios and can be combined with a smart reflector (RIS) to improve system security, thereby enhancing the communication security and versatility of the multi-user MISO-RSMA communication system. On the other hand, this invention designs beamforming and phase-shift vectors to maximize the secure rate for the multi-user MISO-RSMA communication system. This allows for worst-case confidentiality by jointly optimizing the beamforming vectors of the public message, artificial noise, and each user's private message, as well as the phase-shift vector at the RIS. Furthermore, it strengthens the system's resistance to channel uncertainties (performance advantages stemming from simultaneous transmission of public and private messages and the use of artificial noise to reduce eavesdropping performance), thus improving the communication security and robustness of the multi-user MISO-RSMA communication system. Gain

[0081] This invention describes the robust design of secure beamforming vectors and phase shift vectors as a non-convex problem. The optimization problem aims to maximize the minimum security rate achievable in the worst case, but this optimization problem is difficult to solve. Therefore, this invention first introduces slack variables to transform the optimization problem into a tractable form with an equivalent objective function, i.e., a partially decoupled optimization problem. Then, the optimization problem is divided into beamforming vector sub-optimization problems and phase shift vector sub-optimization problems. Finally, the two sub-optimization problems are iteratively optimized alternately until convergence, and the optimal beamforming vector and phase shift vector are output to achieve secure communication. This allows for a fast and effective solution to the optimization problem of maximizing the secure rate of the system, thereby further improving the robustness and communication security of multi-user MISO-RSMA communication systems.

[0082] In practice, the multi-user MISO-RSMA communication system performs rate segmentation on the transmission and reception of user signals.

[0083] This invention divides user messages into public and private parts, then partially decodes the interference and treats the remaining interference as noise. This general architecture is inherently robust to incomplete CSIT.

[0084] A multi-user MISO-RSMA communication system includes an N-antenna base station BS, a smart reflector RIS, K single-antenna legitimate users (user), and a single-antenna eavesdropping party (Eve).

[0085] h ab ,a,b∈{B,E,R,k} is the channel between two nodes in a multi-user MISO-RSMA communication system, where {B,E,R,k} represent the base station, the eavesdropper, the smart reflector, and the k-th user, respectively, and k∈K={1,...,K}, where K is the set of users; RIS helps the base station and communication users improve communication performance.

[0086] Let H ij =diag(h Rj )h iR Let ,ij∈{Bk,BE} represent the joint channel through the intelligent reflector; assuming the channel state information (CSI) from the base station to the user is determined, the CSI from the base station to the eavesdropper is an uncertain channel with an error term, i.e.:

[0087]

[0088] Where: h BE This indicates the direct channel from the base station to the eavesdropping party; This represents the direct channel from the base station to the eavesdropper that the user can obtain; Δh BE ξ represents the error term in the direct channel from the base station to the eavesdropper; h Indicates the error range of the error term; H BE This indicates the joint channel from the base station to the eavesdropping party via RIS; This represents the joint channel between the base station and the eavesdropping party that the user can obtain; ΔH BE ξ represents the error term in the joint channel between the base station and the eavesdropping party; H Indicates the error range of the error term;

[0089] The intelligent reflective surface has M reflective units. Each reflective unit receives the wave signal by adjusting the phase reflection, let v = (v1, v2, ..., v M ) T This represents the phase shift vector of the intelligent reflector. And |v m |=1;

[0090] In the RSMA downlink transmission process of a multi-user MISO-RSMA communication system, the k to be sent th User source information W k Information W is divided into common parts. c,k And private information W p,k Merge all users' public information into a single public information W. c After modulation, a common signal s is obtained. c Then combine the private signals of each user. k The public signal and all users' private signals are linearly precoded and then sent out.

[0091] At the receiving end, after each user receives the signal, they first decode and segment the common signal to obtain their respective common part information. Further employing serial interference cancellation technology to remove common signals and decode private information In turn, complete user information source information can be obtained.

[0092] The multi-user MISO-RSMA communication system designed in this invention can be extended from a two-user communication system to a more general multi-user scenario, and can be combined with a smart reflective surface (RIS) to better improve the system's security performance, thereby further improving the communication versatility and communication security of the multi-user MISO-RSMA communication system.

[0093] In practice, to achieve secure communication, the base station simultaneously transmits signals and artificial noise to improve system security. It's important to note that this artificial noise interferes with both legitimate users and eavesdroppers. Therefore, the received signals for the k-th user and the eavesdropper are as follows:

[0094]

[0095]

[0096] Where, ω c ω k ω z They represent public information s respectively c ,k user's private signal s k The beamforming vector of the artificial noise z, where the encoded signal s i Satisfy E[|s i | 2 ]=1,i∈{c,1,...K}, z is the artificial noise signal transmitted by the base station, satisfying E[|z| 2 ] = 1, n k ,nE ~CN(0,σ 2 ) represents additive Gaussian noise.

[0097] At the receiver of the RSMA system, users first demodulate the common signal, treating all private signals as noise. Then, they demodulate their respective private signals. Due to the use of continuous interference cancellation technology, the common signal does not affect the demodulation of the private signals. Therefore, the received signal-to-noise ratio (SNR) for user k with respect to the common and private parts of the signal can be expressed as follows:

[0098]

[0099]

[0100] Then the reachable rates of user k with respect to public and private signals are R and R, respectively. c,k =α k log2(1+SINR c,k ) and R p,k =log2(1+SINR) p,k ). α k It is the proportion of the signal occupied by user k in the public signal, satisfying... For ease of subsequent analysis, this invention assumes that all users occupy the public signal proportionally, i.e., α k = 1 / K.

[0101] Similarly, we assume that a powerful eavesdropper is familiar with the RSMA communication framework and also wants to decode the public signal before decoding the private signal, just like a legitimate user. Its received signal-to-noise ratios for the public and private signals for user k can be expressed as follows:

[0102]

[0103]

[0104] Its eavesdropping rates for user k's public and private signals are Rk and Rk, respectively. cE,k =α k log2(1+SINR cE ) and R pE,k =log2(1+SINR) pE,k ).

[0105] The reachable safe rate of user k is expressed as:

[0106]

[0107] To ensure fairness for users, the system security rate is represented by the minimum user-achievable security rate.

[0108] In the above formula: hB,k This represents the direct channel from the base station to the k-th user; v H H represents the conjugate transpose of the phase shift vector; B,k σ represents the joint channel from the base station to the k-th user; 2 R represents the variance of the background noise. c,k R p,k R represents the reachable rate of user k with respect to public and private signals; cE,k R pE,k SINR represents the rate of eavesdropping on user k's public and private signals. c,k SINR p,k SINR represents the received signal-to-noise ratio (SNR) of user k's public and private signals. cE SINR pE,k α represents the eavesdropping rate of user k's public and private signals; k This represents the proportion of signal that user k occupies in the public signal, satisfying the following condition: All users share the public signal proportionally, i.e., α. k = 1 / K.

[0109] The objective of this invention is to use the security rate as a security performance indicator for multi-user MISO-RSMA communication systems, by designing the beamforming vector ω. c ω k ω z The optimization problem of maximizing the safe rate is expressed by the following formula (P1): [Formula omitted for brevity]

[0110]

[0111] In the formula: ω represents the achievable safe rate for user k; c ω k ω z They represent public information s respectively c ,k user's private signal s k Beamforming vector of artificial noise z; v m C1 represents the phase shift vector of the m-th intelligent reflector unit; C1 represents the total power constraint of the base station, whose maximum total power is P. S C2 represents the phase shift constraint of the smart reflector.

[0112] This invention designs beamforming vectors and phase shift vectors for multi-user MISO-RSMA communication systems to maximize security rate. This allows for worst-case confidentiality by jointly optimizing the beamforming vectors and phase shift vectors at the RIS for public messages, artificial noise, and each user's private messages. Furthermore, it enhances the system's resistance to channel uncertainties (performance advantages come from the simultaneous transmission of public and private messages and the use of artificial noise to reduce eavesdropping performance), thereby further improving the communication security and robustness of multi-user MISO-RSMA communication systems.

[0113] In the specific implementation process, firstly, slack variables are introduced into the optimization problem (P1). Transform (P1) into an optimization problem with partial variable decoupling (P2):

[0114]

[0115] in: μ c , μ p,k These are introduced slack variables used to equivalently transform (P1) into an optimization problem (P2). The optimal solution of optimization problem (P2) is equivalent to that of (P1). At the optimal solution, the equation C3-C6 holds; otherwise, it can always be solved by increasing... and decrease by 1 / μ c 1 / μ p,k Make the equation true; objective function With variable Since it is monotonically increasing, optimizing the maximum value of the objective function is equivalent to optimizing the maximum value of its variables. It should be noted that the worst user rate requires logarithmic calculation of the optimization result.

[0116] Remove [ ] from the optimization target + This does not affect the joint design of the optimal beamforming vector and the reflection coefficient vector. It is necessary to perform a non-negative operation on the final public signal security rate and private signal security rate values.

[0117] Secondly, slack variables t and r are introduced into the optimization problem of partial variable decoupling (P2). c,k r c1,k r p,k t c,k β cE t cE t p,k β pE,k and t pE The optimization problem of generating partial variable decoupling (P3):

[0118]

[0119] Where: t, r c,k r c1,k r p,k t c,k β cE t cE t p,k β pE,k and t pE These are introduced slack variables used to convert (P2) into an equivalent optimization problem (P3). The optimal solution of optimization problem (P3) is equivalent to the optimal solution of (P2). The objective function in (P2) is equivalent to the objective function in (P3) and C7a, C7b, C7c, and C7d. The objective function value of (P3) needs to be obtained by performing a logarithmic operation. C3 is equivalent to C3a and C3b, C4 is equivalent to C4a, C4b, and C4c, C5 is equivalent to C5a and C5b, and C6 is equivalent to C6a, C6b, and C6c.

[0120] This invention addresses the difficulty of solving optimization problems by introducing slack variables to transform the optimization problem into a tractable form with an equivalent objective function, i.e., a partially decoupled optimization problem. This enables a fast and effective solution to the optimization problem of maximizing the safe speed of the system.

[0121] In the specific implementation process, the optimization problem of partial variable decoupling (P3) is divided into beamforming vector quantum optimization problem and phase shift vector quantum optimization problem;

[0122] Given the phase shift vector v, and designing a robust beamforming vector based on this, we obtain the following beamforming vector sub-optimization problem (P4):

[0123]

[0124] When the beamforming vector ω c ω k and ω z Given that, based on this, designing the optimal reflection coefficient vector yields the following phase shift vector sub-optimization problem (P5):

[0125]

[0126] This invention addresses the difficulty of solving optimization problems by dividing the partially decoupled optimization problem into a beamforming vector quantum optimization problem and a phase shift vector quantum optimization problem. The original optimization problem is then solved by solving the two sub-optimization problems separately, thereby enabling a fast and effective solution to the optimization problem of maximizing the safe speed of the system.

[0127] In the specific implementation process, the infinite number of constraints and non-convex constraints of the beamforming vector quantum optimization problem (P4) are approximated by convex approximation, and the equivalent transformation is obtained to obtain the sub-optimization problem (P4). ω );

[0128]

[0129] In the formula: It is obtained by approximating the infinite number of constraints and non-convex constraints C3a-C7d; u 4ah u 4aH u 4bh u 4bH u 6ah,k u 6aH,k u 6bh u 6bH This indicates the introduced variable.

[0130] Specifically, the beamforming vector quantum optimization problem is solved through the following steps (P4):

[0131] 1) The left side of the non-convex constraint C3a is processed using the SCA algorithm based on the first-order Taylor inequality, while the right side of the non-convex constraint C3a has non-convex variable coupling. Since the variable coupling is on the right side of the ≥ constraint, the Sequential Parametric Convex Approximation (SPCA) algorithm can be used for processing. That is, in the nth iteration of the non-convex constraint C3a, the following... replace;

[0132]

[0133] A 3a,k =(h B,k +v H H B,k )ω c ;

[0134] In the formula: Re represents taking the real part of the complex number; v represents the conjugate transpose of the common signal beamforming vector in the nth iteration; (n),H This represents the conjugate transpose of the phase shift vector in the nth iteration; t represents the slack variable in the nth iteration; c,k This refers to the previously introduced slack variables;

[0135] 2) The equivalent representation of the non-convex constraint C3b is:

[0136] in,

[0137]

[0138] Using Schuler's complement theorem, the nonconvex constraint C3b is transformed into the following...

[0139]

[0140] 3) C4a on the left and |(h BE +vHH BE )ω z | 2 For the infinite number of constraints caused by the uncertainty of CSI, and Substituting into C4a, |(h BE +v H H BE )ω j | 2 value at the nth iteration The lower bound can be represented as:

[0141]

[0142] in:

[0143]

[0144]

[0145] A j D j Z j d 1,j d 2,j α j z 1,j d j z j x BE This represents an intermediate variable, obtained through calculations using known variables and the variable to be solved; v (n),* This represents the conjugate of the phase shift vector in the nth iteration; v represents the private signal beamforming vector for the nth iteration; T This represents the transpose of the phase shift vector; This represents the direct product operation; vec * This indicates the straightening conjugate operation; c H This represents the conjugate transpose of variable c; c * Indicates the conjugate of variable c; c T This represents the transpose of variable c; c (n) Let c represent the nth iteration;

[0146] Similarly, |(h BE +v H H BE )ω z| 2 value at the nth iteration The lower bound can be represented as:

[0147]

[0148] in:

[0149]

[0150]

[0151] Then C4a is converted to:

[0152] in,

[0153] A 4a α 4a β represents an intermediate variable, obtained through calculations using known variables and the variable to be solved; cE This represents the slack variables introduced in the optimization problem;

[0154] ||Δh BE ||≤ξ h and ||ΔH BE ||≤ξ H Convert to the following expression:

[0155]

[0156] Therefore, the variable u is introduced. 4ah ≥0 and u 4aH ≥0, the final expression of C4a transformation is as follows LMI replacement:

[0157]

[0158] in, u 4ah Indicates the introduced variable; I N and I MN These represent identity matrices of dimensions N and MN, respectively.

[0159] 4) For infinitely many constraints C4b, the equivalent expression is:

[0160]

[0161] in,

[0162] Using Schuler's complement theorem, C4b is transformed into:

[0163]

[0164] Will and After substitution, a slack variable u is introduced. 4bh ≥0 and u 4bH ≥0, combined with ||Δh Jk ||≤ξ h and ||ΔH Jk ||≤ξ H C4b is equivalent to the following LMI replacement:

[0165]

[0166] in, Temp 4b =t cE -σ 2 -u 4bH Mu 4bh ;

[0167] 5) The right side of the non-convex constraint C4c has a non-convex variable coupling μ. c t cE The SPCA algorithm is used for processing. During the nth iteration, the following... Replace:

[0168]

[0169] in, and Let β represent the optimal solution of the variables in the (n-1)th recursive optimization; cE This represents the slack variables introduced in the optimization problem;

[0170] 6) Since the non-convex constraint C5a is similar to C3a, the nth iteration of C5a is governed by the following... replace:

[0171]

[0172] Among them, A 5a,k =(h B,k +v H H B,k )ω k , v (n) and These are the optimal solutions for the variables in the nth and (n-1)th recursive optimizations, respectively;

[0173] 7) Since the non-convex constraint C5b is similar to C3b, the nth iteration of C5b is governed by the following... replace:

[0174]

[0175] in,

[0176] 8) Since the non-convex constraint C6a is similar to C4a, C6a is transformed into:

[0177]

[0178] in,

[0179] Further introduce variable u 6ah,k ≥0 and u 6aH,k If ≥0, then during the nth iteration of C6a, the following applies: replace:

[0180]

[0181] in,

[0182] 9) The non-convex constraint C6b is similar to C4b, but a slack variable u is introduced. 6bh ≥0 and u 6bH ≥0, combined with ||Δh Jk ||≤ξ h and ||ΔH Jk ||≤ξ H C6b uses equivalent LMI replacement:

[0183]

[0184] 10) Non-convex constraints C6c are similar to C4c, and are handled using the SPCA algorithm. The following is used in the nth iteration: Replace:

[0185]

[0186] in, and It is the optimal solution for the variable in the (n-1)th recursive optimization; β pE,k This represents the slack variables introduced in the optimization problem;

[0187] 11) To reduce the computational complexity of constraints C7a, C7c, and C7d, they are further transformed using equivalent methods. Since r c,k ,r p,k , μ c , μp,k All are numbers greater than 0, and t, r c1,k ,r p,k r c1,k r p,k Since they are all greater than 1, they are equivalently represented as follows: and Second-order cone constraint:

[0188]

[0189] In summary, the infinite number of constraints and non-convex constraints of the beamforming vector quantum optimization problem (P4) have been handled, and the nth recursive optimization problem is finally expressed as a sub-optimization problem (P). ω ):

[0190]

[0191] By performing a convex approximation on the infinite number of constraints and non-convex constraints of the phase-shift vector suboptimal problem (P5), an equivalent transformation is obtained to obtain the suboptimal problem (P5). v );

[0192]

[0193] In the formula: It is obtained by approximating the infinite number of constraints and non-convex constraints C3a-C7d; u 4ah u 4aH u 4bh u 4bH u 6ah,k u 6aH,k u 6bh u 6bH This indicates the introduced variable.

[0194] Specifically, the phase-shift vector quantum optimization problem (P5) is calculated through the following steps:

[0195] 1) For the non-convex constraint C2, introduce a slack variable vector a = (a1, a2, ..., a2) M ) T ,a i ≥0 and b=(b1,b2,...,b M ) T ,b i For values ​​≥0, the SCA algorithm can be used for approximate solutions.

[0196]

[0197] In the nth iteration, v represents... iTo find the optimal solution, in order to ensure that the slack variables a and b tend to 0, we introduce a penalty parameter λ5, adding -λ5(||a||+||b||) to the objective function, so that the objective function is further transformed into maxt-λ5(||a||+||b||).

[0198] 2) Equivalent expression of non-convex constraint C4b The upper left part is related to the optimization variable v, and only this part of the expression needs to be retained. Therefore, an equivalent expression can be used. C4b The LMI is replaced by the LMI whose dimensions change from (K+2N+3)×(K+2N+3) to (K+3)×(K+3);

[0199]

[0200] 3) Non-convex constraints C6b and C4b are similar and can be represented by equivalent constraints. C6b The LMI is replaced by the LMI whose dimensions change from (K+2N+2)×(K+2N+2) to (K+2)×(K+2);

[0201]

[0202] Except for C4b and C6b, the other constraints are the same as those in problem (P4), and the same processing techniques as in the previous section are used.

[0203] It is important to emphasize that when optimizing the reflection coefficient vector, the beamforming vector ω c ω k , ω z , All of them are known, fixed values.

[0204] In summary, after addressing the infinite number of constraints and non-convex constraints in the phase-shift vector suboptimal problem (P5), the nth recursive optimization problem is finally expressed as a suboptimal problem (P... v ):

[0205]

[0206] This invention addresses the difficulty of solving optimization problems by applying convex approximations to the infinite and non-convex constraints of the beamforming vector quantum optimization problem (P4) and the phase shift vector quantum optimization problem (P5), respectively, transforming them into sub-optimization problems (P... ω ) and sub-optimization problems (P) v This allows us to solve sub-optimization problems (P) separately. ω ) and sub-optimization problems (P) v This allows us to solve the original optimization problem quickly and effectively, thus enabling us to solve the optimization problem of maximizing the safe rate of the system.

[0207] In practice, secure communication is achieved through the following steps:

[0208] S501: Set n=0, cutoff parameter ε, and maximum number of loops, and initialize the randomized phase shift vector v. (0) ;

[0209] S502: For a given phase shift vector v (n) Solve the sub-optimization problem (P) ω To obtain the optimal beamforming vector.

[0210] S503: For a given beamforming vector Solving the sub-optimization problem (P) v To obtain the optimal phase shift vector v * and target value t (n) =t * ;

[0211] S504: Execute n = n + 1, let v (n) =v * ;

[0212] S505: If If the maximum number of loops is reached, proceed to step S506; otherwise, proceed to step S502.

[0213] S506: Output the optimal phase shift vector v * and beamforming vector And correspondingly, in the base station design beamforming vector Designing phase shift vector v in intelligent reflective surfaces * To achieve secure communication.

[0214] This invention addresses the difficulty of solving optimization problems by providing a solution to the sub-optimization problem (P). ω ) and sub-optimization problems (P) v The system performs alternating iterative optimization until convergence and outputs the optimal beamforming vector and phase shift vector to achieve secure communication. This enables the system to quickly and effectively solve the optimization problem of maximizing the secure rate, thereby further improving the robustness and communication security of the multi-user MISO-RSMA communication system.

[0215] To better illustrate the advantages of the technical solution of the present invention, the following experiments are disclosed in this embodiment.

[0216] In this experiment, the system simulation was performed using Matlab software, and the optimization problem was solved using the CVX software package.

[0217] In this experiment, such as Figure 2 As shown, on a two-dimensional coordinate plane, the position of BS is fixed at (0,0), the position of RIS is fixed at (20,10), and the positions of user and Eve are randomly distributed within a circle with center (100,0) and radius 10. Figure 3 As shown. Typical parameter values ​​by default are as follows: N=4, K=2, M=8, κ=1, cutoff parameter ε=0.01, and maximum loop count N. max =50, channel error ξ h =ξ H =0.00005, attenuation factor α BR =α Rk =2.2, α Bk =4, the power P of BS B =30dBm, σ 2 = -80dBm, initial value It is a random vector of maximum power. v (0) The phase is randomly generated in the range [0, 2π]. The simulation result is the average of 10 random channel iterations.

[0218] To demonstrate the superiority of the secure communication method proposed in this invention for intelligent reflector-assisted MISO-RSMA communication systems, in Figure 3 Comparing the three curves of RSMA, as the channel uncertainty ξ increases from 0 (i.e., perfect channel) to 0.0002 and then to 0.0004, the system security rate decreases sequentially. This indicates that the increase in channel uncertainty reduces the performance of the proposed beam direction algorithm in beamforming. This is because the increase in channel uncertainty reduces the accuracy of beam direction, and the power cannot be accurately pointed to the target user, thereby reducing system performance.

[0219] For the RSMA scheme, as ξ increases from 0 to 0.0002, the average reduction in its security rate is 0.12 bit / s / Hz, while the average reductions for the Multicast and SDMA schemes are 0.15 bit / s / Hz and 0.18 bit / s / Hz, respectively. This indicates that under the same channel error conditions, the proposed RSMA scheme has stronger robustness and a stronger ability to resist channel uncertainties. Its performance advantage comes from the simultaneous transmission of public and private messages and the use of artificial noise to reduce eavesdropping performance.

[0220] Overall, as the number of legitimate recipients increases, the system's security rate decreases accordingly. Figure 4As shown, on the one hand, the total transmission power remains constant, and the more users there are, the less power is allocated to each user; on the other hand, an increase in the number of users increases the interference between users. When the number of intelligent reflection units increases, the system has more resources for optimization, the "artificial channel" capability is enhanced, and the security rate increases accordingly. Specifically, when the number of users is 1, the three schemes are equivalent, therefore the system security rate is equal. As the number of users increases, the decrease in the Multicast scheme is relatively stable because the total rate of the common signal is equally divided among all users; as the number of users increases, the system security rate decreases. For the RSMA and SDMA schemes, when the number of users is small, the interference between users is relatively small, and the performance degradation trend is relatively small. As the number of users further increases, the performance drops sharply because the mutual interference between private signals increases significantly with the number of users. At this point, the performance of RSMA is mainly obtained from the common signal. Starting from the number of intelligent reflection units, the Multicast scheme is not sensitive to this parameter, and the performance difference under M=4 and M=8 conditions is small, while the RSMA and SDMA schemes are more sensitive to this parameter.

[0221] Figure 5 The distribution of transmission power for various signals is depicted as a function of the number of users. When the number of users is small, the interference between users is low, the power of the private signal in the RSMA scheme is relatively high, and the scheme performance is mainly obtained by the precoding of the private signal. When the number of users is 2, it is not even necessary to transmit the common signal. As the number of users increases, the mutual interference between private signals increases significantly, and the power proportion of the common signal in the scheme increases, indicating that the scheme performance is more determined by the precoding of the common signal. The power of artificial noise is a relatively stable value.

[0222] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit the technical solutions. Those skilled in the art should understand that any modifications or equivalent substitutions to the technical solutions of the present invention without departing from the spirit and scope of the present invention should be covered within the scope of the claims of the present invention.

Claims

1. A secure communication method for a smart reflector-assisted MISO-RSMA communication system, characterized in that, include: S1: Establish a multi-user MISO-RSMA communication system assisted by an intelligent reflective surface; S2: For multi-user MISO-RSMA communication systems, establish an optimization problem to maximize the secure rate by designing beamforming vectors and phase shift vectors; S3: By introducing slack variables, the optimization problem is transformed into an optimization problem with partial variable decoupling; S4: The optimization problem with partial variable decoupling is divided into beamforming vector quantum optimization problem and phase shift vector quantum optimization problem; S5: Iteratively optimize the beamforming vector quantum optimization problem and the phase shift vector quantum optimization problem until convergence, output the optimal beamforming vector and phase shift vector, and design the beamforming vector in the base station and the phase shift vector in the smart reflector to achieve secure communication.

2. The secure communication method for a smart reflector-assisted MISO-RSMA communication system as described in claim 1, characterized in that: Multi-user MISO-RSMA communication systems perform rate segmentation on the transmission and reception of user signals.

3. The secure communication method for a smart reflector-assisted MISO-RSMA communication system as described in claim 2, characterized in that: In step S1, the multi-user MISO-RSMA communication system includes a Antenna base station, a smart reflector, One legitimate user with a single antenna and one eavesdropping party with a single antenna; Intelligent reflective surface has Each reflection unit receives the wave signal by adjusting the phase reflection. In the RSMA downlink transmission process of a multi-user MISO-RSMA communication system, the first step is to send... User source information Information divided into public sections and private information Secondly, all users' public information is merged into one public information. After modulation, a common signal is obtained. Private information of each user Private signals are obtained by modulation separately. Finally, the public signal and all users' private signals are linearly precoded and transmitted respectively. At the receiving end, after each user receives the signal, the common signal is first decoded and segmented to obtain their respective common part information. Then, serial interference cancellation technology is used to remove common signals and decode private information. In order to obtain complete user information source information .

4. The secure communication method for a smart reflector-assisted MISO-RSMA communication system as described in claim 1, characterized in that, In step S2, the secure rate is used as the security performance indicator of the multi-user MISO-RSMA communication system. The secure rate is maximized by designing beamforming vectors and phase shift vectors. The optimization problem of maximizing the secure rate is then expressed by the following formula. : ； In the formula: Indicates user The achievable safe rate; , , Representing public information , User's private signal and artificial noise Beamforming vector; Indicates the first Phase shift vector of each intelligent reflection unit; This represents the total power constraint of the base station, whose maximum total power is... ; This represents the phase shift constraint of the intelligent reflective surface; This represents the error term in the direct channel from the base station to the eavesdropper. This represents the error term in the joint channel between the base station and the eavesdropping party; Represents the phase shift vector; in: ； ； ； ； ； ； ； ； ； In the formula: Indicates the base station to the Direct channels for individual users; This represents the conjugate transpose of the phase shift vector; Indicates the base station to the A joint channel for multiple users; The variance of the background noise; , Indicates user Regarding the achievable rates of public and private signals; , Indicates user The rate at which public and private signals are eavesdropped; , Indicates user Signal-to-noise ratio (SNR) of received public and private signals; , Indicates user The rate at which public and private signals are eavesdropped; Indicates user The proportion of signals occupied in the public signal; This indicates the direct channel from the base station to the eavesdropping party; This indicates the joint channel from the base station to the eavesdropping party via RIS.

5. The secure communication method for a smart reflector-assisted MISO-RSMA communication system as described in claim 4, characterized in that, In step S3: First, in the optimization problem Introducing slack variables ,Will Transformed into an optimization problem with partial variable decoupling. : ； in: These are introduced slack variables; optimization problem Optimal solution and optimization problem They are equivalent; Secondly, in the optimization problem of partial variable decoupling Introducing slack variables , , , , , , , , and Optimization problem of partial variable decoupling : ； in: , , , , , , , , and These are introduced slack variables; optimization problem At the optimal solution and The optimal is equivalent; optimization problem The objective function in the problem is equivalent to an optimization problem. The objective function and , , , ; Equivalent to and , Equivalent to , ,and , Equivalent to and , Equivalent to , and .

6. The secure communication method for a smart reflector-assisted MISO-RSMA communication system as described in claim 5, characterized in that, In step S4, the optimization problem of decoupling some variables is addressed. It is divided into beamforming vector quantum optimization problem and phase shift vector quantum optimization problem; When the phase shift vector Given that a robust beamforming vector is designed, the following beamforming vector quantum optimization problem is obtained. : ； When beamforming vector , and Given that the optimal reflection coefficient vector is to be designed, the following phase shift vector sub-optimization problem is obtained. : 。 7. The secure communication method for a smart reflector-assisted MISO-RSMA communication system as described in claim 6, characterized in that, Beamforming vector quantum optimization problem and phase shift vector quantum optimization problem The infinite number of constraints and non-convex constraints are approximated by convexity, and the equivalent transformation yields the sub-optimization problem. Sum optimization problem .

8. The secure communication method for a smart reflector-assisted MISO-RSMA communication system as described in claim 7, characterized in that, The sub-optimization problem can be expressed by the following formula. : ； In the formula: It is an infinite number of constraints and a non-convex constraint. Obtained after convex approximation; This indicates the introduced variable.

9. The secure communication method for a smart reflector-assisted MISO-RSMA communication system as described in claim 7, characterized in that, The sub-optimization problem can be expressed by the following formula. : ； In the formula: It is an infinite number of constraints and a non-convex constraint. Obtained after convex approximation; Indicates the introduced variable; Indicates the first During the next iteration The optimal solution; , Represents a vector of slack variables; , express and Slack variables in.

10. The secure communication method for a smart reflector-assisted MISO-RSMA communication system as described in claim 7, characterized in that, In step S5, secure communication is achieved through the following steps: S501: Settings Cutoff parameters And the maximum number of loops, the initial value of the randomized phase shift vector. ; S502: For a given phase shift vector Solve the sub-optimization problem To obtain the optimal beamforming vector , , ; S503: For a given beamforming vector , , Solve the sub-optimization problem To obtain the optimal phase shift vector and target value ; S504: Execution ,make ; S505: If If the maximum number of iterations is reached, proceed to step S506; otherwise... Execute step S502; S506: Output the optimal phase shift vector and beamforming vector , , And correspondingly, in the base station design beamforming vector , , Design phase shift vectors in intelligent reflective surfaces To achieve secure communication.

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