Energy-efficient STAR-RIS-assisted wireless resource allocation method for NOMA systems

Abstract

The present invention discloses a downlink wireless resource allocation method for a STAR-RIS-assisted NOMA system based on energy efficiency. The present invention first constructs a STAR-RIS-assisted NOMA system model. Secondly, the optimization objective function, variables, and constraints are determined, and the user signal-to-noise ratio is simplified using a quadratic transformation. Based on this, the power allocation problem is converted into a linear form using the Dinkelbach algorithm through alternating optimization, and the phase shift optimization problem is solved by relaxing the rank-one constraint. Finally, the optimal power allocation and phase shift matrices are iteratively solved to maximize system energy efficiency. Compared with the alternating optimization algorithm of the traditional RIS-NOMA system, the present invention improves system energy efficiency, expands communication range, and has faster convergence performance.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communications and relates to a NOMA system resource allocation method assisted by STAR-RIS based on energy efficiency. Background Art

[0002] The Simultaneously Transmissive and Reflective Smart Metasurface (STAR-RIS), an emerging wireless communication technology, is capable of both reflecting and transmitting signals. By adjusting the phase and amplitude of surface elements, STAR-RIS dynamically controls the propagation path of electromagnetic waves, thereby optimizing signal coverage and improving communication quality and energy efficiency. Compared to traditional smart reflective surfaces (RIS), which only reflect signals, STAR-RIS significantly enhances signal strength and communication reliability in the target area.

[0003] Furthermore, non-orthogonal multiple access (NOMA) technology, a key technology in next-generation wireless communication systems, differs from traditional orthogonal multiple access (OMA) in that NOMA improves spectrum efficiency and system capacity by allowing multiple users to transmit data on the same time-frequency resources. Both smart reflectors and NOMA are important technologies in wireless communications. Deploying smart reflectors in appropriate locations can modify the channel propagation environment, enhancing communication performance by reflecting and transmitting signals, reducing transmission power requirements, and improving system energy efficiency.

[0004] Because the combination of NOMA and STAR-RIS can extend communication range, system energy efficiency can be maximized by optimizing resource allocation. Currently, most research on smart reflector-assisted NOMA systems focuses on total system power consumption or system sum rate. However, faced with growing data demands and high energy consumption, future wireless networks urgently need to design greener and more energy-efficient transmission schemes. Therefore, a wireless resource allocation method for STAR-RIS-assisted NOMA systems is urgently needed to address these challenges and improve system energy efficiency. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a STAR-RIS-assisted NOMA downlink resource allocation method, electronic device and readable storage medium based on energy efficiency, which improves the transmission performance by alternately optimizing the power allocation at the base station (BS) and the phase shift matrix of STAR-RIS to maximize the system energy efficiency.

[0006] To solve the above technical problems, the first aspect of the present invention provides a NOMA system wireless resource allocation method assisted by STAR-RIS based on energy efficiency, comprising the following steps:

[0007] Step S1: Establish a STAR-RIS assisted NOMA downlink system;

[0008] Step S2: Propose a joint optimization problem of the transmit power allocation at the BS, and the reflection and transmission phase shift matrices of STAR-RIS;

[0009] Step S3: For the joint optimization problem in step S2, the STAR-RIS reflection and transmission phase shift matrices are fixed, the transmission power allocation problem at the BS is studied, and the optimal power allocation for the user is obtained.

[0010] Step S4: Based on the optimal power allocation obtained in step S3, the reflection and transmission phase shift matrices at STAR-RIS are further optimized using the quadratic form of the user signal-to-interference-plus-noise ratio (SINR) to obtain an optimal solution;

[0011] Step S5: Iteratively optimize the power allocation optimized at the BS in step S3 and the reflection and transmission phase shift matrices at the STAR-IRS in step S4. By iteratively solving the optimal power allocation, reflection and transmission phase shift matrices, and maximizing system energy efficiency, the alternating optimization approach is employed.

[0012] Furthermore, in step S1, in this system, a base station equipped with a single antenna serves two single-antenna users via STAR-RIS, which has N simultaneous transmission and reflection elements. The near-end user, r, is located in the reflection region, and the STAR-RIS operates in reflection mode. Meanwhile, the far-end user, t, is located in the transmission region, and the STAR-RIS operates in transmission mode.

[0013] Furthermore, in step S2, a joint optimization problem is established for power allocation at the base station and STAR-RIS reflection and transmission phase shifts, while satisfying the user's QoS communication requirements. The optimization goal is to maximize the system's energy efficiency. The optimization variables are the base station's transmit power to the user and the reflection and transmission coefficient matrices. The optimization constraints are the base station's total transmitted power, energy conservation, and phase shift angle constraints.

[0014] Furthermore, in step S3, by fixing the STAR-RIS reflection and transmission phase shift matrices, the original optimization problem can be simplified to a power allocation optimization problem. A quadratic transformation is used to convert the SINRs of the reflected and transmitted users into quadratic form. The Dinkelbach algorithm introduces a nonnegative variable to transform the fractional objective function in the optimization problem into a linear form. Finally, standard convex optimization tools are used to solve the problem, yielding the optimal power allocation for each user.

[0015] Furthermore, in step S4, by performing an equivalent transformation on the quadratic form of the user SINR, the reflection and transmission phase shift optimization problem at STAR-RIS is converted into a semi-definite programming (SDP) problem, and then the rank-one constraint is relaxed using the semi-definite relaxation (SDR) technique to obtain the optimal value of the reflection and transmission phase shift matrix at STAR-RIS.

[0016] Furthermore, step S5 specifically includes:

[0017] S501: Initialize the number of iterations, transmission and reflection coefficient matrices and system energy efficiency;

[0018] S502: Repeat the above steps S3 and S4 to obtain the optimal solution of the optimal power allocation and the reflection and transmission phase shift matrix at the STAR-RIS;

[0019] S503: Update the iteration value until the obtained system energy efficiency converges to the accuracy value or reaches the maximum number of iterations.

[0020] The second aspect of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and runnable on the processor, wherein when the processor executes the computer program, an energy-efficiency-based STAR-RIS-assisted NOMA system wireless resource allocation method is implemented.

[0021] The third aspect of the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it implements a STAR-RIS-assisted NOMA system wireless resource allocation method based on energy efficiency.

[0022] Beneficial effects of the present invention: The present invention adopts an alternating optimization method based on quadratic transformation to jointly optimize the base station transmit power and the STAR-RIS reflection and transmission phase shift matrix to maximize system energy efficiency. The specific effects are as follows:

[0023] Compared to the typical RIS-NOMA system model, this paper proposes a simultaneous transmitting and reflecting reconfigurable smart surface (STAR-RIS) technology that enhances communication between users and base stations (BSs). This technology assists the NOMA system and enhances signal strength in the target area. While considering user quality of service and the total transmission power constraints of the BS, it optimizes power allocation and phase shift, improving overall system energy efficiency.

[0024] Compared with traditional alternating optimization methods for resource allocation, this method first simplifies the user's signal-to-noise ratio through a secondary transformation, making each iterative optimization process more efficient and thus accelerating overall convergence. Secondly, alternating optimization using the Dinklebach algorithm and the SDR algorithm achieves better global optimality, effectively avoiding the local optimality problem that traditional methods are prone to.

[0025] The present invention also verifies through experiments and simulations that, for the alternating optimization method of the Dinklebach algorithm and the SDR algorithm based on quadratic transformation, the STAR-RIS-assisted NOMA system shows significant advantages in energy efficiency improvement, convergence speed and global optimality compared with the traditional RIS-assisted NOMA system and the STAR-RIS-assisted OMA system. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a model diagram of the STAR-RIS assisted NOMA downlink system according to an embodiment of the present application.

[0027] Figure 2 This is a flow chart of the resource allocation method of the STAR-RIS assisted NOMA system according to an embodiment of the present application.

[0028] Figure 3 Schematic diagram of the relationship between energy efficiency and number of iterations in an embodiment of the present application.

[0029] Figure 4 Schematic diagram of the relationship between energy efficiency and the number of different STAR-RIS components in the embodiment of the present application. DETAILED DESCRIPTION

[0030] The realization of the objectives, functional features and advantages of the present invention will be further described in conjunction with embodiments and with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0031] like Figure 1 As shown, an embodiment of the present application provides a STAR-RIS-assisted NOMA system downlink wireless resource allocation method based on energy efficiency, including the following steps:

[0032] Step S1: Construct a STAR-RIS-assisted downlink NOMA system model. In this model, a single-antenna base station serves two single-antenna users via a STAR-RIS with N elements. The near-end user, user r, is located in the reflection region, and STAR-RIS operates in reflection mode. Meanwhile, the far-end user, user t, is located in the transmission region, and STAR-RIS operates in transmission mode. It is assumed that the base station has complete knowledge of the CSI of all channels. Furthermore, the significant signal power loss due to multiple RIS reflections is negligible.

[0033] In one example, Figure 1 In the STAR-RIS assisted downlink NOMA system, STAR-RIS is 50m away from the BS, and users r and t are located in a semicircle with a radius of 6m and STAR-RIS as the center. Unless otherwise specified, the number of STAR-RIS elements M = 10, and the QoS requirements of users r and t are The maximum transmission power of the base station is 1W, and the circuit power consumption is set to 1W. The path loss exponent is set to α Sk =α BS =2.2 and α Bk = 2.8, k∈{r,t}, Ricean factor K BS =K Sk =1dB, ρ0=-30dB, and the noise power is set to -80dBm.

[0034] In the STAR-RIS assisted NOMA system, the superposition code will be used for the BS, and the received signal at user k can be expressed as

[0035]

[0036] in, denote the channel matrices between the base station and user k and between STAR-RIS and user k, respectively. represents the channel between BS and STAR-RIS. Assuming that STAR-RIS follows the energy splitting (ES) protocol, the transmission coefficient matrix and reflection matrix of STAR-RIS can be expressed as: is the phase shift between the transmission matrix and the reflection matrix, the amplitude of the transmission signal and the reflected signal amplitude have And the law of conservation of energy must be satisfied, that is, the total energy of the two must be equal to the total energy of the incident signal, so β t +β r =1, is the additive white Gaussian noise received by user k.

[0037] In one example, the channel between user k and BS may be blocked by obstacles, so it is assumed that the channel is Rayleigh distributed, that is, h k obey Since STAR-RIS will be deployed at a location where there are direct lines between user k and the BS, it is assumed that the channels from user k to STAR-RIS and from STAR-RIS to the BS follow the Rice distribution, which can be expressed as

[0038]

[0039] where d BS and d Sk are the distances between BS and STAR-RIS and between STAR-RIS and user k, α BS and α Sk are the corresponding path loss indices respectively; ρ0 represents the path loss when the reference distance is 1m; G LOS and is the LOS component of the corresponding channel, G NLOS and is the random NLOS component of the corresponding channel, which obeys Rayleigh fading; K BS and K Sk is the Rice factor.

[0040] According to the system model, user r is assumed to be the near-end user and user t is the far-end user. According to the basic principle of NOMA, user r will first decode the signal of user t and subtract it from the signal it receives. At the same time, the far-end user t directly decodes its own signal by treating the signal of user r as interference. The SINR of user r and user t is obtained as

[0041]

[0042] According to SIC, the signal-to-noise ratio of user r decoding user t is

[0043]

[0044] Therefore, the achievable rates for user r and user t are expressed as

[0045] R k =log2(1+γ k ),k∈{r,t} (7)

[0046] Step S2: Under the condition that the user's QoS communication requirements are met, the joint optimization problem of power allocation and STAR-RIS phase shift at the BS is established as follows:

[0047]

[0048] Among them, P r and P t They are respectively represented as the transmission power allocated to the reflection user and the transmission user, P c It is represented by the fixed circuit power consumption of the system. Constraints 1-3 are the QoS constraints of the users. represents the minimum SINR required by the QoS of user k, constraint 4 is the constraint on the total transmit power, constraint 5 is the energy conservation constraint, and constraint 6 is the constraint on the phase shift angle.

[0049] The optimization problem above involves a non-convex problem with coupled variables P and Θ, making it difficult to solve directly. However, the optimization variables can be decoupled using the principle of alternating optimization. This involves splitting the optimization problem into two subproblems: optimizing the base station's transmit power and optimizing the STAR-RIS phase shift. These subproblems can then be optimized separately.

[0050] Step S3: Fix the STAR-RIS reflection and transmission phase shift matrices. The original problem can be simplified to a power allocation optimization problem:

[0051]

[0052] Since energy efficiency is the ratio of system sum speed to total system power, the system sum speed in the numerator of the objective function is a function of log, and the log function is non-convex and non-decreasing, maximizing R t +R r This is equivalent to maximizing γ t +γ r .

[0053] Since the quadratic transform can transform complex nonlinear optimization problems into more tractable linear or quadratic forms, it simplifies the problem-solving process and reduces computational complexity. Furthermore, the quadratic transform can transform non-convex optimization problems into convex ones, thus ensuring convergence to the global optimal solution. The SINR of the reflected and transmitted users are simplified using the quadratic transform as follows:

[0054]

[0055] in, obj r and obj t denote the quadratic forms of the SINR of the reflected user and the transmitted user respectively. Then the original optimization problem can be transformed into

[0056]

[0057] The numerator of the objective function in the above optimization problem is a concave function about P, ​​and the denominator is a linear function. It can be solved by the Dinkelbach algorithm, which converts the fractional objective function in the optimization problem into a non-fractional form by introducing an additional non-negative variable The new optimization problem can be expressed as follows:

[0058]

[0059] The above problem is a linear programming problem, and the objective function is is monotonically decreasing and has a unique zero. Since problem (13) is convex, it can be solved using convex optimization tools (such as CVX) to obtain the optimal power allocation.

[0060] In one example, Figure 2 As shown in the figure, the user's SINR is first transformed twice, and then the Dinklebach algorithm is used to solve the power allocation optimization problem in its transformed quadratic form, and the SDR algorithm is used to solve the phase shift optimization problem. Finally, the system energy efficiency is obtained through alternating iterative optimization.

[0061] Step S4: Based on the user optimal power obtained in step S3, the original optimization problem (8) can be transformed into the following phase shift optimization problem:

[0062]

[0063] Similarly, the SINR of the reflected user and the transmitted user is converted into a quadratic form using a quadratic transform, and further to simplify the objective function:

[0064] First, let as well as It can be concluded that

[0065] reintroduction Transform the problem into

[0066]

[0067] The above problem is a semidefinite programming problem, and only the rank-one constraint is non-convex. By relaxing the rank-one constraint through SDR technology, it can be solved directly through CVX, and then the optimal solution of the reflection and transmission phase shift matrices at STAR-RIS is obtained.

[0068] Step S5: Jointly optimize the optimal power allocation at the BS in step S3 and the reflection and transmission phase shifts at the STAR-IRS in step S4 to obtain the optimal energy efficiency of the system. Specifically, in this scheme, the application first initializes the number of iterations iter, the transmission and reflection coefficient matrices and the energy efficiency; then, given Perform a secondary transformation on the user's SINR and use Dinklebach to solve the power allocation optimization problem to obtain Next, the power sent by the given base station to user r and t is The user's SINR is obtained by solving the optimization problem of the reflection and transmission phase shift matrices at the STAR-RIS that has been converted into the SDP form in the secondary transformation form. Finally, through the Calculate the energy efficiency EE and update the iteration value until the optimal solution converges to the accuracy ξ = 10 -3 or the maximum number of iterations is reached.

[0069] An embodiment of the present application also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and runnable on the processor. When the processor executes the computer program, it implements an energy-efficiency-based STAR-RIS-assisted NOMA system wireless resource allocation method.

[0070] An embodiment of the present application further provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it implements a STAR-RIS-assisted NOMA system wireless resource allocation method based on energy efficiency.

[0071] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned energy-efficiency-based STAR-RIS-assisted NOMA system wireless resource allocation methods.

[0072] The effects of the above embodiments of the present application are Figure 3 、 Figure 4 It is confirmed in.

[0073] exist Figure 3 In the figure, the variation of energy efficiency with the number of iterations is shown, where the number of iterations refers to the number of alternating optimizations. It can be seen from the figure that the energy efficiency of the STAR-RIS-assisted NOMA system is always higher than that of the traditional RIS-assisted NOMA system and the STAR-RIS-assisted OMA system, and both have basically reached the convergence conditions within 2 times, indicating the fast convergence performance of the proposed alternating optimization method.

[0074] Figure 4 The relationship between the number of STAR-RIS elements and energy efficiency is shown. It's easy to see that increasing the number of STAR-RIS elements improves system energy efficiency. However, in practical applications, considering hardware costs, a reasonable number of STAR-RIS elements should be used to improve system transmission performance.

[0075] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0076] The above embodiments are intended only to illustrate the technical solutions of the present invention and are not intended to limit the same. Although the present invention has been described in detail through the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present invention may be modified or replaced with equivalents without departing from the spirit and scope of the present invention, and such modifications or replacements should be included within the scope of the claims of the present invention.

Claims

1. A wireless resource allocation method for NOMA system assisted by STAR-RIS based on energy efficiency, characterized in that: The method comprises the following steps: Step S1: Establishing a STAR-RIS assisted NOMA downlink system; Step S2: Under the condition that the QoS communication requirements of the user are met, a joint optimization problem of the transmission power allocation at the BS, the reflection matrix and the transmission matrix of STAR-RIS is given; The optimization goal of the joint optimization problem is to maximize the energy efficiency of the system. The optimization variables are the base station's transmission power to the user and the reflection and transmission coefficient matrices. The optimization constraints are the total power constraint of the base station's transmission, the energy conservation constraint, and the phase shift angle constraint. Step S3: By fixing the STAR-RIS reflection and transmission phase shift matrices, the joint optimization problem is simplified to a power allocation optimization problem. At the same time, the signal-to-interference-noise ratios of the reflection and transmission users are converted into quadratic forms using a quadratic transformation. Non-negative variables are introduced to convert the fractional objective function in the optimization problem into a linear form. Standard convex optimization tools are used to solve the problem and obtain the optimal power allocation for each user. Step S4: Optimizing the reflection and transmission phase shift matrices at STAR-RIS based on the optimal power allocation obtained in step S3 and utilizing the quadratic form of the user's signal-to-interference-and-noise ratio; Specifically, by performing an equivalent transformation on the quadratic form of the user signal-to-interference-plus-noise ratio (SINR), the reflection and transmission phase shift optimization problem at STAR-RIS is transformed into a semidefinite programming problem. The rank-one constraint is then relaxed using a semidefinite relaxation technique to solve for the optimal values ​​of the reflection and transmission phase shift matrices at STAR-RIS. Step S5: Iteratively optimize the power allocation optimized at the BS in step S3 and the reflection and transmission phase shift matrices at the STAR-IRS in step S4; through the idea of ​​alternating optimization, iteratively solve the optimal power allocation, reflection and transmission phase shift matrices to maximize system energy efficiency.

2. The energy-efficiency-based STAR-RIS-assisted NOMA system wireless resource allocation method according to claim 1, characterized in that: In step S1, in the NOMA downlink system, a base station equipped with a single antenna serves two single-antenna users through STAR-RIS, and the STAR-RIS has N simultaneous transmission and reflection unit elements; wherein the near-end user is located in the reflection area and the far-end user is located in the transmission area.

3. The energy-efficiency-based STAR-RIS-assisted NOMA system wireless resource allocation method according to claim 1, characterized in that: Step S5 specifically includes: S501: Initialize the number of iterations, transmission and reflection coefficient matrices and system energy efficiency; S502: Repeat the above steps S3 and S4 to obtain the optimal solution of the optimal power allocation and the reflection and transmission phase shift matrix at the STAR-RIS; S503: Update the iteration value until the obtained system energy efficiency converges to the accuracy value or reaches the maximum number of iterations.

4. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, it implements the energy-efficiency-based STAR-RIS-assisted NOMA system wireless resource allocation method as described in any one of claims 1 to 3.

5. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the energy-efficiency-based STAR-RIS-assisted NOMA system wireless resource allocation method as described in any one of claims 1 to 3 is implemented.

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