Energy-efficient smart reflector-assisted NOMA uplink transmission method

Abstract

The present invention discloses a smart reflector-assisted NOMA uplink transmission method based on energy efficiency. The present invention first establishes an IRS-assisted NOMA uplink system; and designs a joint optimization problem of transmit power control at the user and phase shift matrix optimization at the IRS to maximize the energy efficiency of the uplink system; then the block coordinate descent method is used to decouple the optimization variables in the joint optimization problem, and each variable is optimized separately, that is, the joint optimization problem is divided into two sub-problems of user power control and IRS phase shift matrix optimization; finally, the above two sub-problems are solved in sequence until the accuracy difference of the solution reaches a preset accuracy or the number of iterations reaches a certain number. The present invention can maximize the number of data bits transmitted per hertz under unit energy and measure it by energy efficiency.

Description

Technical Field

[0001] The present invention belongs to the field of mobile communication technology, and in particular relates to an energy-efficiency-based smart reflective surface-assisted NOMA uplink transmission method. Background Art

[0002] Intelligent Reflecting Surface (IRS) technology is an emerging wireless communication technology. IRS uses a large number of reconfigurable reflective elements to form a passive reflective panel. Through the different changes in the phase of the incident signal by each element, precise reflective beamforming is achieved collaboratively. However, unlike existing active relays, IRS only controls the changes in the signal phase of each reflective array element to reconstruct the wireless channel state. Its processing process does not use additional energy for decoding, channel estimation, and data transmission. Through appropriate passive beamforming design, the reflected signal passing through the IRS can be coherently superimposed with the signals of other paths to enhance the power of the desired signal, and can also destructively cancel each other to reduce interference signals from different channels. Due to its advantages in energy efficiency, cost, and flexibility, IRS technology is regarded as an important component of the future mobile communications field, aiming to achieve more efficient and intelligent wireless communication networks.

[0003] Non-Orthogonal Multiple Access (NOMA) is an advanced multiple access technology designed to improve the spectral efficiency of wireless communication systems. Unlike traditional orthogonal multiple access technologies (such as CDMA and OFDMA), NOMA allows multiple users to transmit on the same time, frequency, and codeword resources. Through power allocation and signal superposition, non-orthogonal transmission between users is achieved. The receiver uses advanced signal processing techniques, such as Successive Interference Cancellation (SIC), to separate individual user data from the mixed signal. NOMA is considered a key technology for future mobile communications because it significantly improves system capacity and user experience.

[0004] Considering the potential advantages of IRS and NOMA technologies, the integration of IRS and NOMA is a promising solution in the next generation of mobile communication systems. Therefore, this paper considers an IRS-assisted NOMA uplink system. Among the various existing schemes, there are some studies on maximizing the system sum rate and minimizing the transmission power. For example, by jointly optimizing the decoding order, power allocation, active beamforming, transmit beamforming and reflection beamforming, the system sum rate can be maximized and the transmission power can be minimized. Unlike focusing only on maximizing the system sum rate or minimizing the transmission power, the goal of this application is to optimize the balance between sum rate and power consumption in the uplink NOMA-IRS link system, thereby maximizing the number of data bits transmitted per hertz per unit energy. This goal can be measured by energy efficiency, which is a key performance indicator in green communications. Summary of the Invention

[0005] The main purpose of this invention is to address the energy efficiency deficiencies of existing NOMA-IRS systems and provide a highly energy-efficient uplink transmission scheme, assuming that there is no direct link due to the presence of obstacles. Under the constraints of minimum transmission rate, maximum user transmit power, and IRS phase shift matrix, a joint optimization method for user transmit power control and IRS phase shift matrix optimization is proposed to maximize the system's energy efficiency.

[0006] A 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 1: Establish an IRS-assisted NOMA uplink system;

[0008] Step 2: Design a joint optimization problem of transmit power control at the user and phase shift matrix optimization at the IRS to maximize the energy efficiency of the uplink system;

[0009] Step 3: Decouple the optimization variables in the joint optimization problem using a block coordinate descent method, and optimize each variable separately, that is, divide the joint optimization problem into two sub-problems: user power control and IRS phase shift matrix optimization;

[0010] Step 4: For the first subproblem, the user power control problem, the diagonal phase shift matrix of the IRS is fixed. The Dinkelbach algorithm is used to transform the fractional programming problem in the user power control problem into a series of parameterized linear programming problems by introducing additional non-negative variables. The optimal solution is gradually approached to obtain the optimal user power control vector p.

[0011] Step 5: For the second sub-problem, namely, the phase shift matrix optimization problem at the IRS, after obtaining the user's power control vector p in step 4, fix the control vector p and use the semi-positive definite relaxation SDR algorithm to optimize the IRS phase shift matrix and solve it;

[0012] Step 6: Repeat steps 4 and 5 until the accuracy difference of the solution reaches the preset accuracy or the number of iterations reaches a certain number.

[0013] The second aspect of the present invention provides an energy-efficiency-based smart reflective surface-assisted NOMA uplink transmission device, comprising a memory, a processor, and a computer program stored on the memory and runnable on the processor. When the processor executes the computer program, the method of smart reflective surface-assisted NOMA uplink transmission is implemented.

[0014] The third aspect of the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method of smart reflective surface-assisted NOMA uplink transmission.

[0015] Beneficial effects of the present invention:

[0016] Compared with the traditional uplink NOMA-IRS model, this invention not only considers the situation where the direct link cannot transmit due to obstruction, but also establishes an uplink NOMA-IRS transmission model with obstruction specifically for this situation. In addition, given that existing solutions mainly focus on system sum rate maximization and power minimization, while less research is done on system energy efficiency, this invention proposes an optimization scheme that balances sum rate maximization and power minimization in the uplink NOMA-IRS link, thereby maximizing the number of data bits transmitted per hertz per unit energy and measuring it through energy efficiency.

[0017] Unlike traditional uplink NOMA-IRS systems, which jointly optimize user transmit power and IRS reflective beamforming before calculating energy efficiency, this method innovatively utilizes the Dinkelbach algorithm, introducing variables to transform complex fractional functions into linear functions to solve the problem. Convergence analysis of the algorithm demonstrates that this method not only converges quickly but also significantly improves energy efficiency compared to existing methods.

[0018] The present invention also verifies through experiments and simulations that the proposed NOMA-IRS algorithm using the Dinkelbach algorithm for power control and the beamforming optimization of the SDR algorithm as the optimization shows significant advantages in energy efficiency improvement, convergence speed and global optimality compared with the traditional NOMA-IRS algorithm and the random phase shift NOMA-IRS algorithm, while the energy efficiency is only about 10% lower than the upper bound. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is an uplink diagram of IRS-assisted NOMA with obstacles in an embodiment of the present application;

[0020] Figure 2 This is the energy efficiency convergence diagram of the optimization algorithm used in the embodiment of the present application;

[0021] Figure 3 This is a graph showing the relationship between the number of reflective units and energy efficiency in an embodiment of the present application;

[0022] Figure 4 This is a graph showing the relationship between the number of users and energy efficiency in an embodiment of the present application;

[0023] Figure 5 This is a diagram showing the relationship between the number of users and the total system rate in an embodiment of the present application. DETAILED DESCRIPTION

[0024] 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.

[0025] The present application provides an energy-efficient smart reflector-assisted NOMA uplink transmission method, which includes the following steps:

[0026] Step 1: Establish an IRS-assisted NOMA uplink system. Assume that there is no direct link between the base station and the user due to the presence of obstacles. The transmitted signal from the base station must be reflected by the IRS to be received by the user.

[0027] further, Figure 1 This is the IRS-assisted NOMA uplink system model constructed in step 1. Assume that there is no direct link between the base station and the user due to the presence of obstacles. The base station's transmitted signal must be absorbed by the user through reflection from the IRS. K (K ≥ 2) users are each equipped with a single antenna. All users transmit signals to the base station (BS) using the same time-frequency resource block. At the same time, the IRS deployed on the building wall adds a reflection channel between the user and the BS. The number of IRS reflection units is N. It is assumed that the BS knows all the state information (CSI) of all channels.

[0028] The received signal of BS is expressed as:

[0029]

[0030] in, is the channel vector from IRS to BS, is the channel vector from user k to IRS, is the diagonal phase shift matrix of IRS, q n ∈[0,2π), t k is the unit power user signal from user k, p k is the transmit power of user k, n is the additive Gaussian white noise at the BS, with mean 0 and variance s 2 , that is, n~CN(0,s 2 ).

[0031] All channel paths are represented by path loss and small-scale fading, that is, d -a / 2 s, where d is the propagation distance between transceivers, α is the path loss factor, and s is the small-scale fading.

[0032] Assume that the channel between the user and the BS is blocked by obstacles and does not exist. Therefore, the channel from the base station to the user is only the reflected channel of the IRS. Since the IRS will be deployed in a position where there is a direct line between it and user k and the BS, it is assumed that the channel vector h between user k and the IRS is k,I , and the channel vector h from IRS to BS I,B All obey the Rice distribution:

[0033]

[0034] in, and is the line-of-sight (LoS) path, and are non-line-of-sight (NLoS) paths and each of them obeys CN(0,1). λ is the Rice factor, which represents the relative weight of the LoS component in the total channel. Its value range is between 0 and 1.

[0035] In a preferred embodiment, in the NOMA uplink system, in order to reduce the interference of other users to the user, the BS uses continuous interference cancellation (SIC) to obtain the user signal. Users with better channel conditions tend to decode earlier. Here, the effective channel of user k is It depends on the unknown phase shift matrix Θ. Therefore, the effective channel cannot be used to sort the users. To solve this problem, this embodiment removes the unknown parameter Θ from the effective channel and considers the simplified effective channel Without loss of generality, we further assume that users are arranged in descending order of simplified effective channels, with user 1 having the best channel condition and user K having the worst, i.e. According to the NOMA protocol, the signal-to-interference-and-noise ratio (SINR) of user k can be expressed as:

[0036]

[0037] In the above formula, the numerator is the received signal strength of user k, and the denominator is the interference signal from user (k+1) to user K and the noise signal. Then, the achievable information rate of user k is R k =log2(1+G k ). The sum rate of the entire system is expressed as follows:

[0038]

[0039] Step 2: Propose a joint optimization problem of transmit power control at the user and phase shift matrix optimization at the IRS to jointly optimize and maximize the energy efficiency of the uplink system.

[0040] The optimization problem in this embodiment is modeled as follows:

[0041]

[0042]

[0043] Where p=[p1,...,p K ] T is the user power control vector, p s is the fixed circuit power consumption of the system, The minimum communication rate requirement for users, is the minimum decoding SINR. The C1 constraint satisfies the service quality requirements of each user, the C2 constraint requires that the transmission power of each user does not exceed its own maximum transmission power, and the C3 constraint is the phase shift constraint of the IRS reflection unit, which is equivalent to

[0044] Step 3: For the joint optimization problem in step 2, the objective function is a non-convex optimization problem with coupled variables p and Θ, making it difficult to solve directly. In this embodiment, the Block Coordinate Descent (BCD) method is used to decouple the optimization variables and optimize each variable separately. This means that the optimization problem is divided into two sub-problems: user power control and IRS phase shift matrix optimization. These are then solved using the Dinkelbach algorithm and the SDR semi-definite relaxation algorithm, respectively.

[0045] Step 4: For the first sub-problem of the user power control problem in step 3, fix the diagonal phase shift matrix Θ of the IRS and use the Dinkelbach algorithm by introducing an additional non-negative variable The fractional programming problem in the user power control problem is transformed into a series of parameterized linear programming problems, gradually approaching the optimal solution to obtain the optimal user power control vector p. Specifically:

[0046] By fixing the phase shift matrix Θ at the IRS, the original optimization problem is transformed into a pure user power control problem:

[0047]

[0048] The constraints of the above optimization problem are all convex, because the constraint C1 can be expressed as:

[0049]

[0050] Therefore, only the objective function is non-convex, and the numerator in the objective function is a concave function about p, and the denominator is a linear function. The Dinkelbath algorithm can be used to introduce a non-negative variable Transforming the fraction into a non-fractional form, the new optimization problem is expressed as follows:

[0051]

[0052] For simplicity, the numerator of the original objective function is expressed as f(p) and its denominator is expressed as g(p), then the optimization expression is According to the Dinkelbach algorithm, we only need to find The only zero point of .

[0053]

[0054] Step 5: For the second sub-problem in step 3, the phase shift matrix optimization problem at the IRS, after obtaining the user's power control vector p through step 4, fix the vector p and convert the IRS phase shift matrix optimization into a semi-positive definite optimization. The classic semi-positive definite relaxation SDR algorithm is used for the IRS phase shift matrix optimization, and the standard convex optimization tool CVX is used for optimization and solution. The rank-one constraint is not considered first. If the solution satisfies the rank-one constraint, the optimal vector is obtained. If not, the maximum eigenvalue and maximum eigenvector are taken to obtain an approximate solution. Specifically:

[0055] After obtaining the user's power control vector p through optimization in step 4, the original optimization problem can be simplified to the following phase shift optimization problem:

[0056]

[0057] For easy handling This embodiment first rearranges the phase shift matrix Θ into a vector It contains elements Obviously, w contains all the information of Θ. Then, an auxiliary vector is introduced The operation e represents the Hadamard product. Therefore, the intractable square of the above equation is converted into a matrix product form:

[0058]

[0059] The problem can be reformulated as:

[0060]

[0061] Reintroduce an auxiliary matrix: H is a positive semidefinite matrix, and the objective function becomes maxw H Hw, the objective function is still non-convex at this time, because the goal is to maximize a quadratic function with a semi-positive matrix. To solve this problem, first constrain the element Relax to the spherical constraint ||w||2=N, ||w||2 represents the Euclidean norm of w. When w=Nx, we get w H The maximum value of Hw, where x is the normalized eigenvector of H with respect to the largest eigenvalue λ. In addition, by w H Hw=lN 2 x H x=lN 2 The optimal solution is given. Since the feasible region is expanded by relaxation, λN 2 It is the upper bound.

[0062] Because of w H Hw=Tr(w H Hw)=Tr(HwwH ), where Tr(X) represents the trace of X. This embodiment introduces a new variable, W=ww H , it is obvious that W is a semi-positive definite matrix, that is, W30 and rank(W)=1. After performing the above operations, the optimization problem is transformed into a semi-positive definite programming problem:

[0063]

[0064] in, There is only one non-zero element, which is located at the (i, i)th position and is equal to 1. That is, B i (i,i)=1 and B i The rest of the elements of are zero. Therefore, Tr(B i W)=W(i,i)=|w i | 2 =1, which is consistent with The C1 constraint is the user minimum power constraint, the C2 constraint is the reflection vector constraint, the C3 constraint is that the elements of W are not less than 0, and the C4 constraint is a rank-one constraint. The above optimization problem is a semidefinite program, and the only non-convex constraint is the rank-one constraint C4.

[0065] Ignoring the C4 constraint for now, we can get the following relaxed version. This is a convex optimization problem that can be solved using the existing CVX package in MATLAB. The optimal solution obtained is denoted as W * The question now is how to * Convert to a feasible solution. If rank(W * )=1, then W * =w * w *H , w * will be a feasible solution and also the optimal solution. On the other hand, if rank(W * )>1, you need to start from W * Extract the feasible solution w * An effective method is to choose W * The eigenvector corresponding to the largest eigenvalue of the eigenvector of provides the best rank-one approximation. * The maximum eigenvalue of and its associated eigenvector are λ1 and q1 respectively. Then It can be used as a candidate solution. If w is not a feasible solution, it is necessary to map w to a nearby feasible solution, which can be achieved by normalizing each element of w to 1.

[0066] Step 6: Repeat steps 4 and 5 until the accuracy difference e of the solution reaches the preset accuracy or the number of iterations reaches a certain number, obtain the current energy efficiency value, set the number of Monte Carlo simulations M, loop the calculation M times and calculate the average value as the current optimal energy efficiency value.

[0067] In general, the proposed power control using the Dinkelbach algorithm and the beamforming optimization using the SDR algorithm are summarized in Algorithm 2.

[0068]

[0069] The embodiment of the present application also discloses an energy-efficiency-based smart reflective surface assisted NOMA uplink transmission device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for smart reflective surface assisted NOMA uplink transmission are implemented. The memory may include a memory, such as a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk memory, etc. The processor, the network interface, and the memory are interconnected through an internal bus, which may be an industrial standard architecture bus, a peripheral component interconnection standard bus, an extended industrial standard structure bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory is used to store programs. Specifically, the program may include program code, and the program code includes computer operating instructions. The memory may include memory and non-volatile memory, and provides instructions and data to the processor.

[0070] The embodiment of the present application further discloses a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the method for implementing the smart reflective surface assisted NOMA uplink transmission are implemented. Specifically, the computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. The volatile memory may include random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include read-only memory (ROM), hard disk, flash memory, optical disk, magnetic disk, etc.

[0071] At this point, the energy efficiency calculation scheme in the NOMA-IRS uplink system has been completed. In order to further illustrate the effectiveness of the high-efficiency IRS-assisted NOMA uplink transmission scheme based on energy efficiency, the embodiment of this application is simulated and verified below.

[0072] For comparison, the performance curves of various transmission schemes are plotted at the same time: the NOMA-up algorithm with relaxed extended feasible region is used as the upper bound for comparison; the NOMA-IRS algorithm is optimized using the Dinkelbach algorithm for power control and the SDR algorithm for beamforming optimization; the traditional NOMA-IRS is used with traditional power allocation and SDR beamforming optimization; the NOMA-IRS with random phase shift is used with random beamforming optimization that only performs power control without phase shift optimization; and the OMA-IRS uses the orthogonal frequency division multiple access technology OMA as the transmission scheme.

[0073] The distances between the BS and IRS, and between the IRS and user, were randomly generated uniformly within the range of 50m and 100m, respectively. Small-scale fading between the BS and IRS, and between the IRS and user, was modeled using Rician (k factor of 5) and Rayleigh fading, respectively. The path loss exponents and large-scale path losses corresponding to these two channels were 2.4, 2.8, and 30+24log10(d), 30+28log10(d), respectively, where d is the distance in meters. The LoS path fractional factors were 0.8 and 0.8, respectively, with a bandwidth of B = 1MHz and a noise power spectral density of N0 = -174dBm / Hz. Circuit power consumption was 20dBm, and the convergence accuracy was set to 0.01. Unless otherwise stated, all results are averaged over 100 Monte Carlo simulations.

[0074] Figure 2 The figure shows the energy efficiency convergence comparison of the optimization algorithms. The energy efficiency value of the optimized NOMA-IRS basically does not change after three iterations, which shows a good convergence property. The energy efficiency achieved is also higher than that of the traditional NOMA-IRS and the random phase-shifted NOMA-IRS. The energy efficiency of the traditional scheme is significantly increased after one alternating optimization, and the convergence conditions are basically met. NOMA-up and OMA-IRS are the results of one calculation, and the number of alternating optimization iterations is not changed, so the curve is not given.

[0075] Figure 3 This is the curve of energy efficiency versus the number of IRS reflector units. This embodiment sets the maximum transmit power of all users. The minimum transmission rate requirement for each user is set to 20dBm. The figure shows that energy efficiency increases with the number of reflectors, as more reflectors increase the power of user signals received by the BS. Furthermore, regardless of whether the IRS phase shift is optimized, the NOMA system consistently outperforms the OMA system. The figure clearly shows that the scheme with optimized IRS phase shift significantly improves energy efficiency compared to the scheme with random phase shift. Given a fixed number of 16 reflectors, the optimized NOMA-IRS scheme achieves a 39.9% improvement in energy efficiency compared to the traditional scheme and an 85% improvement compared to the random scheme, demonstrating the importance of optimizing the IRS phase shift. Furthermore, the optimized scheme is twice as efficient as the OMA scheme, and even the upper bound of the scaling constraint is only about 10% lower, demonstrating the excellent performance of the optimized scheme. Even the IRS-OMA scheme with random phase shift performs better than the NOMA scheme without IRS, demonstrating the importance of deploying IRS in communication systems.

[0076] Figure 4 and Figure 5 They are the relationship curves between the number of users and energy efficiency and the relationship curves between the number of users and the total rate. In this embodiment, the number of IRS reflective antennas is set to 32, and the other settings remain unchanged. Figure 4 As can be seen from the figure, the energy efficiency values ​​of all schemes tend to decrease as the number of users increases. This is because as the number of users increases, the signal of each user will generate more interference in the uplink. NOMA separates multi-user signals through the power domain, but each user needs to share the same spectrum resources with other users. More users means more interference, which will reduce the signal-to-interference-and-noise ratio (SINR) of each user. Figure 5 It can be seen that the summed rates for all considered schemes increase with the number of users k. However, for larger values ​​of k, the increase decreases. This behavior can be explained by the concavity of the logarithmic function in the rate expression, which leads to a decrease in the average rate per user, thereby reducing the overall energy efficiency of the system.

[0077] Therefore, it is necessary to select the appropriate number of users to ensure the total rate while also ensuring some energy efficiency. Figure 4 As can be seen from the energy efficiency curve, the percentage drop in energy efficiency between 2-3 users and 3-4 users is 26% and 12.5% ​​respectively. Therefore, to ensure that the energy efficiency does not drop too much, 2 users is the optimal choice. However, considering the actual situation, 3 users are selected in this article to ensure sufficient energy efficiency while meeting certain rate requirements.

[0078] It should be understood that the above description of the preferred embodiment is relatively detailed and cannot be regarded as limiting the scope of protection of the patent of the present invention. Under the guidance of the present invention, ordinary technicians in this field can make substitutions or modifications without departing from the scope of protection of the claims of the present invention, which fall within the scope of protection of the present invention. The scope of protection requested by the present invention shall be based on the attached claims.

Claims

1. Energy-efficiency-based smart reflector-assisted NOMA uplink transmission method, characterized by The method comprises the following steps: Step 1: Establish an IRS-assisted NOMA uplink system; Step 2: Design a joint optimization problem of transmit power control at the user and phase shift matrix optimization at the IRS to maximize the energy efficiency of the uplink system; Step 3: Decouple the optimization variables in the joint optimization problem using a block coordinate descent method, and optimize each variable separately, that is, divide the joint optimization problem into two sub-problems: user power control and IRS phase shift matrix optimization; Step 4: For the first subproblem, the user power control problem, the diagonal phase shift matrix of the IRS is fixed. The Dinkelbach algorithm is used to transform the fractional programming problem in the user power control problem into a series of parameterized linear programming problems by introducing additional non-negative variables. The optimal solution is gradually approached to obtain the optimal user power control vector p. Step 5: For the second sub-problem, namely, the phase shift matrix optimization problem at the IRS, after obtaining the user's power control vector p in step 4, fix the control vector p and use the semi-positive definite relaxation SDR algorithm to optimize the IRS phase shift matrix and solve it; Step 6: Repeat steps 4 and 5 until the accuracy difference of the solution reaches the preset accuracy or the number of iterations reaches a certain number.

2. The energy-efficiency-based smart reflector-assisted NOMA uplink transmission method according to claim 1, characterized in that: In the uplink system, the transmission signal of the base station is absorbed by the user through reflection of the IRS.

3. The energy-efficiency-based smart reflector-assisted NOMA uplink transmission method according to claim 2, characterized in that: Each user is equipped with a single antenna, and all users transmit signals to the base station using the same time-frequency resource block. At the same time, the IRS deployed on the building wall adds a reflection channel between the user and the base station.

4. The energy-efficiency-based smart reflector-assisted NOMA uplink transmission method according to claim 1, characterized in that: The signal to interference plus noise ratio (SINR) of each user in the uplink system is a ratio, the numerator of which is the received signal strength of the user, and the denominator is the sum of interference signals of other users and noise signals.

5. The energy-efficiency-based smart reflector-assisted NOMA uplink transmission method according to claim 4, characterized in that: The achievable information rate R of user k k Characterized by R k =log2(1+G k ), where Γ k Indicates the signal-to-interference-and-noise ratio (SINR) of the user.

6. The energy-efficiency-based smart reflector-assisted NOMA uplink transmission method according to claim 5, characterized in that: The joint optimization problem in step 2 is modeled as: where p k is the transmission power of user k, p s is the fixed circuit power consumption of the system, and K is the total number of users.

7. The energy-efficiency-based smart reflector-assisted NOMA uplink transmission method according to claim 6, characterized in that: The joint optimization problem has the following three constraints: Constraint 1: Satisfy each user's quality of service requirements; Constraint 2: The transmit power of each user must not exceed its own maximum transmit power; Constraint 3: Phase shift constraint on the IRS reflection unit.

8. The energy-efficiency-based smart reflector-assisted NOMA uplink transmission method according to claim 1, characterized in that: In step 5, the standard convex optimization tool CVX is used for optimization.

9. Energy-efficient smart reflector-assisted NOMA uplink transmission equipment, characterized in that: It includes a processor and a computer-readable storage medium, wherein the computer-readable storage medium stores instructions. When the instructions are executed by the processor, the method for smart reflective surface assisted NOMA uplink transmission as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for smart reflective surface assisted NOMA uplink transmission as described in any one of claims 1 to 7 is implemented.

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