STAR-RIS assistance-based resource optimization method for RSMA communication and sensing integrated system

Abstract

The invention provides a resource optimization method of an RSMA (Received Signal Moving Average) communication and sensing integrated system based on STAR-RIS assistance. The method comprises the following steps: constructing a cellular communication system transmission model assisted by a reconfigurable intelligent surface; establishing a weighted sum rate maximization robust resource allocation problem based on a cellular communication system transmission model; setting a robust constraint, and constructing a resource allocation problem with a constraint condition based on the robust constraint; converting a resource allocation problem into a convex problem, and converting a robust constraint into a deterministic constraint; carrying out alternative optimization solution on the converted convex problem to obtain a resource allocation scheme; according to the method, the influence of the uncertainty of a transmission link channel is considered, a multi-antenna communication system is established, and the communication quality of a user and the sum rate of the maximized system can be ensured to the maximum extent; according to the system, ideal channel state information is considered, and high robustness is achieved; and the system performance can be further improved by using the RSMA and STAR-RIS technologies.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of wireless communication, and particularly relates to a resource optimization method of a RSMA communication and sensing integrated system based on STAR-RIS assistance. BACKGROUND

[0002] With the rapid development of wireless communication technology, people's requirements for the performance of communication systems are getting higher and higher. While meeting the demand for high speed and large capacity communication, the integration of communication and sensing has become a hot research topic. The integrated sensing and communication system can effectively utilize spectrum resources and improve the overall performance of the system. In the integrated communication and sensing system, how to further improve the spectral efficiency and energy efficiency of the system is a key problem to be solved.

[0003] Rate splitting multiple access (RSMA) is a non-orthogonal multiple access technology that can effectively improve the spectral efficiency of the system by splitting the user's message into multiple sub-messages for transmission. As an extension of non-orthogonal multiple access (NOMA) technology, RSMA has greater flexibility and overcomes the limitations of NOMA in decoding complexity and interference management. At the same time, the emergence of reconfigurable intelligent surface technology provides a new way to optimize the wireless communication environment. Among them, the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) as a new type of reconfigurable intelligent surface (RIS) has higher degrees of freedom and can simultaneously realize signal reflection and transmission, providing the possibility for further improving system performance. Existing researches mostly use NOMA and RIS technologies for resource optimization in the integrated communication and sensing system, but the above methods have certain limitations in interference management and degrees of freedom. And the existing work is completed under ideal channel conditions. In fact, due to the complex electromagnetic environment and channel delay, it is difficult for the base station to obtain accurate channel gain.

[0004] In summary, RSMA and STAR-RIS technologies can further improve system performance, and channel uncertainty should be considered. Therefore, a resource robust optimization method for the integrated communication and sensing system based on STAR-RIS and RSMA is proposed. SUMMARY

[0005] To solve the problems in the prior art, the application provides a resource optimization method for an RSMA all-sensing integrated system based on STAR-RIS assistance, which comprises the following steps: constructing a reconfigurable intelligent surface assisted cellular communication system transmission model; establishing a weighted sum rate maximization robust resource allocation problem based on the cellular communication system transmission model; setting a robust constraint, and constructing a resource allocation problem with a constraint condition based on the robust constraint; converting the resource allocation problem into a convex problem, and converting the robust constraint into a deterministic constraint; and performing alternating optimization on the converted convex problem to obtain a resource allocation scheme.

[0006] The application has the following beneficial effects:

[0007] The application considers the influence of transmission link channel uncertainty, establishes a multi-antenna communication system, and can maximize the communication quality of users and the sum rate of the system; the system has strong robustness compared with a system considering ideal channel state information; and the system performance can be further improved by using RSMA and STAR-RIS technologies. BRIEF DESCRIPTION OF DRAWINGS

[0008] Figure 1 A flowchart of the resource optimization method for the RSMA all-sensing integrated system based on STAR-RIS assistance of the application;

[0009] Figure 2 A schematic diagram of the system model of the application. DETAILED DESCRIPTION

[0010] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the application.

[0011] A resource optimization method for an RSMA all-sensing integrated system based on STAR-RIS assistance, which comprises the following steps: constructing a reconfigurable intelligent surface assisted cellular communication system transmission model; establishing a weighted sum rate maximization robust resource allocation problem based on the cellular communication system transmission model; setting a robust constraint, and constructing a resource allocation problem with a constraint condition based on the robust constraint; converting the resource allocation problem into a convex problem, and converting the robust constraint into a deterministic constraint; and performing alternating optimization on the converted convex problem to obtain a resource allocation scheme.

[0012] In the application, the following representation method is adopted: represents transposition of a vector or a matrix . represents a complex vector or complex matrix performing conjugate transpose; denotes the modulus of a complex number; denotes the 2-norm of a vector; denotes the Frobenius norm of a matrix; denotes vector diagonalization; denotes the trace of a matrix; denotes matrix vectorization; denotes the eigenvector corresponding to the largest eigenvalue of ; denotes the Kronecker product; denotes a unit matrix of dimension .

[0013] A STAR-RIS assisted RSMA omnirange integration system robust rate optimization method, as shown in Figure 1 , includes:

[0014] S1: constructing a STAR-RIS assisted cellular communication system transmission model;

[0015] S2: establishing a weighted sum rate maximization robust resource allocation problem;

[0016] S3: converting the non-convex problem and the robust constraint into a convex problem and a deterministic constraint;

[0017] S4: alternately optimizing and solving the two converted sub-problems to obtain a resource allocation scheme.

[0018] A STAR-RIS assisted multiple-input single-output cellular communication system transmission model, as shown in , includes: a full-duplex base station equipped with transmitting antennas and receiving antennas, a STAR-RIS containing transmitting and reflecting elements, a single-antenna user, and a single-antenna sensing target. Figure 2

[0019] The RSMA scheme involves dividing the message sent to the thuser (denoted as ) into two parts: all users are combined into a common stream by using a common shared codebook, and the beamforming vector of the common stream is denoted as . The private part of the thuser is encoded using a private codebook known only to the base station and itself, and is transmitted to the base station using the common stream The beamforming vector associated with a private message of a user is denoted as . Moreover, the dedicated radar signal transmitted for sensing the target is , and the beamforming vector is denoted as .

[0020] STAR-RIS contains elements that are both transmissive and reflective. Each element adopts an ES mode, splitting the input signal into two segments, which are then redirected to the transmission and reflection spaces to perform respective communication and sensing tasks. The transmissive t and reflective r coefficient matrices of STAR-RIS are denoted as , . Where STAR-RIS vector , represents the amplitude of the th STAR-RIS element, represents the phase shift value of the th STAR-RIS element, where . To maintain the principle of energy conservation, the amplitude adjustment of all STAR-RIS elements should satisfy a specific criterion, i.e.,

[0021]

[0022] and denote the th element of STAR-RIS vector.

[0023] The users are located at the front of STAR-RIS to receive the reflected signals, while the targets are located at the back of STAR-RIS to capture the transmitted signals. The received signal of the th user can be expressed as:

[0024]

[0025] where, denotes the communication channel between STAR-RIS and the th user, denotes the communication link from BS to STAR-RIS, denotes the reflection coefficient matrix of STAR-RIS, denotes the additive white Gaussian noise with at the th user. The cascaded link from DFRC-BS to the user through STAR-RIS is denoted as , so the received signal of the user is finally written as:

[0026]

[0027] For the users, the common message should be decoded first by sharing the common codebook among users where the private message and the sensing signal are considered as interference, the user decodes the common message The achievable rate of

[0028]

[0029] Subsequently, the common message is subtracted from the received signal by successive interference cancellation, ensuring that the private message can be decoded independently, avoiding the interference from the common message, thus, the user decodes its designated private message The achievable rate of

[0030]

[0031] Moreover, to ensure that all users successfully decode the common message, the achievable rate of each user should exceed the allocated data rate. This means that, for each user, to accurately detect its designated information from the common message, the following constraint should be satisfied:

[0032]

[0033] where denotes the actual data rate allocated to the th user.

[0034] The integrated sensing and communication signal transmitted by the base station reaches the probing target through the refraction path facilitated by the STAR-RIS. Subsequently, the echo signal, after reflecting from the target, returns to the base station through a similar path. By defining as the channel link from the STAR-RIS to the target, and as the transmission coefficient matrix associated with the STAR-RIS.

[0035]

[0036] where denotes the AWGN at the BS, , denotes the sensing received interference, , the clutter can be modeled as signal-independent, thus its covariance matrix is constant, .

[0037] By defining the sensing cascade channel The output signal-to-interference-plus-noise ratio is given by

[0038]

[0039] Considering channel uncertainty, a non-convex, multi-variable coupled robust resource allocation problem is established to maximize the system weighted sum rate, while satisfying the maximum transmit power, STAR-RIS constraint, RSMA constraint, and user QoS constraint. Specifically, a bounded channel error model is considered, i.e.

[0040]

[0041] where and are the base station-to-user and sensing target concatenated channel estimation values, respectively; and are the base station-to-user and sensing target concatenated channel estimation errors, respectively; and are the base station-to-user and sensing target concatenated channel gain estimation errors, respectively.

[0042] Based on the above analysis, the robust resource allocation problem of maximizing the system weighted sum rate can be described as:

[0043]

[0044] where , , , C1 is the power constraint, C2 is the rank-one constraint, C3 is the positive semi-definite constraint, C4 is the RSMA rate constraint, C5 is the user QoS constraint, C6 and C7 are the STAR-RIS constraints, C8 is the sensing constraint, and C9 is the uncertainty constraint.

[0045] It is not difficult to prove that when the base station only transmits a communication waveform, the same weighted sum rate as the combination of transmitting a communication waveform and a dedicated sensing waveform can be achieved, and there is no need to optimize .

[0046] Finally, the solution is obtained, and a new set of solutions can be reconstructed. The mathematical model can be described as:

[0047]

[0048]

[0049] ​​​

[0050] where is the cascaded channel from the base station to the STAR-RIS and from the STAR-RIS to the user.

[0051] The original problem is decomposed into two sub-problems, and sub-problem 1 can be reformulated as

[0052]

[0053] To deal with the non-convexity of the objective function, an auxiliary variable is introduced, and the original problem is reformulated as

[0054]

[0055] where and are the common and private rates considering channel uncertainty, respectively. To solve the non-convexity of C11 and C13, auxiliary variables are introduced, which represent the signal-to-interference-plus-noise ratio (SINR) of the common and private rates, respectively. Therefore, the above two constraints can be transformed as

[0056]

[0057] C16 and C18 can be rewritten as

[0058] Introducing auxiliary variables , transformed into

[0059]

[0060] Similarly, introducing auxiliary variables , transformed into

[0061]

[0062] It is worth noting that equations (2), (3), (5), and (6) are non-convex, but their right sides are convex functions. Therefore, we can apply an iterative algorithm based on continuous convex approximation to deal with these difficulties. For this purpose, first-order Taylor approximation is performed on the non-convex elements to obtain the corresponding lower bounds. The following equations can be obtained.

[0063]

[0064] For the C14 constraint, introducing auxiliary variables , it can be transformed into

[0065]

[0066] The subproblem PB1 can be reformulated as

[0067]

[0068] By means of matrix transformation , one can obtain where , , , . And by means of . The constraints C19, C20, C21, C22, C25, C26, can be transformed into

[0069] By means of , one can obtain Let , , , . Introduce nonnegative slack variables , , , , , . By generalized S-procedure, the elimination of the uncertainties can be obtained as

[0070]

[0071] where , generalized S-procedure: define a quadratic function in terms of the variables :

[0072] The LMI can be obtained as

[0073]

[0074] The rank-one constraint has the following equivalent D.C. form

[0075]

[0076] The above equations (7) (8) are nonconvex, and the suboptimal solution is obtained iteratively using successive convex approximation. By applying first-order Taylor expansion, a convex upper bound of the above equations can be obtained. Let where

[0077] where is a penalty factor. The final subproblem PB1 is transformed into

[0078]

[0079] For sub-problem PB2, it can be expressed as

[0080]

[0081] Similarly, the penalty function is introduced to deal with the rank-one constraint. By adding this penalty term to the objective function, where , is the penalty factor. The optimization problem can be rewritten as:

[0082] Solve by alternating optimization method, that is:

[0083] Step 1, fix STAR-RIS phase shift , , the method of penalty function is used to deal with the rank-one constraint, and the base station active beam optimization sub-problem is converted into a convex semi-definite programming problem;

[0084] Step 2, fix the active beam , , the method of penalty function is used to deal with the rank-one constraint, and the STAR-RIS passive beam optimization sub-problem is converted into a convex semi-definite programming problem;

[0085] Step 3, use CVX toolbox to solve the above two convex semi-definite programming problems to get the optimal , , and value, that is, the resource allocation scheme.

[0086] The above examples further illustrate the purpose, technical solutions and advantages of the present application. It should be understood that the above examples are only preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made to the present application within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A resource optimization method for an RSMA sensory integration system based on STAR-RIS assistance, characterized in that, include: Construct a reconfigurable smart surface-assisted cellular communication system transmission model; establish a weighted sum rate maximization robust resource allocation problem based on the cellular communication system transmission model; Set robust constraints, construct a resource allocation problem with constraints based on the robust constraints, transform the resource allocation problem into a convex problem, and transform the robust constraints into deterministic constraints; perform alternating optimization on the transformed convex problem to obtain the resource allocation scheme.

2. The resource optimization method for a STAR-RIS-assisted RSMA inductive integrated system according to claim 1, characterized in that, The reconfigurable smart surface-assisted cellular communication system transmission model includes: a system equipped with Root transmitting antenna, A full-duplex base station with a root receiving antenna, one containing STAR-RIS is a component that can both transmit and reflect light. A user with a single antenna, and a target being sensed with a single antenna.

3. The resource optimization method for a STAR-RIS-assisted RSMA inductive integrated system according to claim 2, characterized in that, The reconfigurable smart surface-assisted cellular communication system transmission model adopts downlink transmission mode, with one equipped with The base station with one antenna transmits data to a device containing a direct link. STAR-RIS is a component that can both transmit and reflect light. A single antenna user transmits information to sense a single antenna target; however, the direct line-of-sight link between the base station and the communication user, and between the sensed target, is blocked by obstacles and cannot transmit.

4. The resource optimization method for a STAR-RIS-assisted RSMA inductive integrated system according to claim 3, characterized in that, The uncertainty of the transmission link channel is: ； in, and From base station to the Concatenated channel estimates for individual users and perceived targets; and From base station to the The channel estimation error of individual users and perceived targets in a concatenated channel; and For base station to the Upper bound of channel gain estimation error for individual users and perceived targets.

5. The resource optimization method for a STAR-RIS-assisted RSMA inductive integrated system according to claim 1, characterized in that, The resource allocation problem with constraints is as follows: ； in, , , , Representing public flow beamforming vector, For the first Beamforming vectors associated with a user's private messages. For dedicated radar beamforming vectors, Represents conjugate transpose; A given weight corresponding to the priority of each user; For the first Private speed for each user for Shared speed for individual users Indicates assignment to the first The actual data rate per user For the first Minimum rate threshold for each user This represents the maximum power of the system. To sense SINR, For sensing threshold; For the STAR-RIS transmission vector, the first One element, For the STAR-RIS reflection vector, the first Each element.

6. The resource optimization method for a STAR-RIS-assisted RSMA inductive integrated system according to claim 1, characterized in that, Transforming the weighted sum rate maximization robust resource allocation problem into a convex problem and converting robust constraints into deterministic constraints includes using continuous convex approximation methods, generalized S-Procedure methods, and first-order Taylor expansions to transform non-convex problems and robust constraints with parameter perturbations into convex problems and deterministic constraints, respectively.

7. The resource optimization method for a STAR-RIS-assisted RSMA inductive integrated system according to claim 6, characterized in that, Transforming the resource allocation problem into a convex problem: Step 1: When the base station only transmits communication waveforms, the same weighted sum rate is achieved as when transmitting a combination of communication waveforms and dedicated sensing waveforms; no optimization is necessary. ; Step 2: Introduce non-negative auxiliary variables to transform the non-convex objective function optimization problem into a convex function optimization problem. Step 3: Using the first-order Taylor expansion, obtain the linear lower bound of the non-convex constraint, and then approximate the corresponding lower bound using an iterative algorithm based on continuous convex approximation.

8. The resource optimization method for a STAR-RIS-assisted RSMA inductive integrated system according to claim 6, characterized in that, Converting robust constraints into deterministic constraints involves: converting the robust constraints into matrices, transforming the matrices, and then using the generalized S-Procedure method to convert the robust constraints with parameter perturbations into deterministic constraints based on the transformed matrices.

9. A resource optimization method for a STAR-RIS-assisted RSMA inductive integrated system according to claim 8, characterized in that, Transformations of a matrix include: ； ； in, for The trace of a matrix product , , , All are matrices. for Matrix transpose For matrix Vectorized transpose, For Kronecker product, Vectorize matrix B. The transpose of the vectorized conjugate transpose of matrix G. For matrix Vectorized conjugate transpose.

10. The resource optimization method for a STAR-RIS-assisted RSMA inductively coupled system according to claim 1, characterized in that, Alternating optimization solutions to the transformed convex problem include: Step 1: Fix IRS phase shift , By using the penalty function method to handle the rank-one constraint, the active beam optimization subproblem of the base station is transformed into a convex semi-positive definite programming problem. Step 2: Fix the active beam , By using the penalty function method to handle rank-one constraints, the STAR-RIS passive beam optimization subproblem is transformed into a convex semi-positive definite programming problem. Step 3: Use the CVX toolbox to solve the two convex semi-positive definite programming problems mentioned above to obtain the optimal solution. , , and Value, i.e., resource allocation scheme.

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