Intelligent reflecting surface assisted non-orthogonal multiple access system power allocation method and system

Abstract

The application is aimed at scenarios such as smart grids, and users are easily blocked by obstacles. A smart reflecting surface assisted non-orthogonal multiple access system power distribution method and system for ultra-reliable low-latency users are proposed, specifically including establishing a communication system model between the base station, the smart reflecting surface and the ultra-reliable low-latency users; according to the current smart reflecting surface phase shift vector and power distribution and decoding order, taking the sum rate of users as the optimization target, the optimal reflecting surface phase shift vector and power distribution are obtained; the reflecting surface phase shift vector satisfying the rank constraint is obtained according to the Gaussian randomization; whether the condition of completing iteration is met is judged. The advantages of the smart reflecting surface extended coverage and the non-orthogonal multiple access system throughput are combined, the reliability of the system is improved, and the total rate performance of the user is improved.

Description

Technical Field

[0001] This invention designs a power allocation method and system for an orthogonal multiple access system, which is particularly applicable to a power allocation method for an IRS-assisted orthogonal multiple access system for ultra-reliable low-latency users used in the field of mobile communication technology. Background Technology

[0002] With the rapid increase in internet user access demands and the capacity growth brought about by advanced multimedia applications, the requirements for data rates are constantly rising, necessitating communication systems to improve their ability to process large-scale data. Non-orthogonal multiple access (NOAMI) technology can serve multiple users on the same resource block, offering higher spectral efficiency compared to NOAMI technology and effectively improving system throughput. In applications such as medical robots, autonomous vehicles, and factory automation, systems need to respond within milliseconds, thus placing high demands on real-time performance. Ultra-Reliable Low Latency Communication (URLLC), a key service of 5G, enables highly reliable, low-latency end-to-end communication. According to 3GPP requirements, the transmission reliability of a single data frame should reach 99.9%, and the end-to-end latency should be less than 1ms.

[0003] In wireless communication links, signal quality is degraded due to path loss, reflection, refraction, and scattering caused by objects such as buildings. To ensure the quality of the received signal, we can increase the transmission power, but this also reduces power efficiency. In recent years, with the development of electromagnetic materials technology, intelligent reflective surfaces made of artificial electromagnetic material thin films have emerged. Intelligent reflective surfaces can be electronically controlled through integrated electronic devices to manipulate the reflection of signals in any direction, thereby making the propagation environment controllable. Currently, there is a lot of research on the combined application of intelligent reflective surfaces and ultra-reliable low-latency (ULLS) technology, but further research is needed on the combination of intelligent reflective surface-assisted non-orthogonal multiple access systems with ULLS technology. Summary of the Invention

[0004] Purpose of the invention: To address the shortcomings of the above-mentioned technologies, this invention proposes a power allocation method for a non-orthogonal multiple access system that effectively improves the coverage and throughput of communication systems, utilizes intelligent reflective surfaces to provide communication links for ultra-reliable low-latency users, and is easy to deploy.

[0005] Technical Solution: To achieve the above technical objectives, the present invention provides a power allocation method for a nonorthogonal multiple access system assisted by an intelligent reflector, comprising the following specific steps:

[0006] A non-orthogonal multiple access system model is established, consisting of a base station, a smart reflector, and ultra-reliable low-latency users. The base station uses superposition coding to send signals to users. When the direct link between the base station and the user is blocked, the smart reflector directly reflects the signal from the base station to add a reliable communication link for the user.

[0007] Based on the non-orthogonal multiple access system model, the base station transmit power allocation, intelligent reflector phase shift vector, and decoding order are jointly designed. The optimal power allocation and intelligent reflector phase shift vector require optimization of the sum of all user rates.

[0008] The decoding order is designed based on the distance and priority between the user and the reflector, and the optimal power allocation and intelligent reflector phase shift matrix are obtained by alternately optimizing the power allocation and intelligent reflector phase shift matrix.

[0009] Gaussian randomization is applied to the optimal intelligent reflector phase shift matrix to obtain the corresponding intelligent reflector phase shift vector. It is then determined whether the conditions for completing the iteration are met. If they are met, the iteration ends.

[0010] Preferably, the intelligent reflector-assisted nonorthogonal multiple access system model is as follows:

[0011] Assume the base station is configured with a single antenna, and the K ultra-reliable low latency (URLLC) users are also configured with single antennas. The base station's transmitted signal is:

[0012]

[0013] Where s k p represents the transmitted signal of user k. k This represents the transmit power of user k.

[0014] The signal received by user k is:

[0015]

[0016] Where g and h k z represents the channel state information between the base station and the smart reflector, and between the smart reflector and user k, respectively. k This represents the Gaussian white noise signal received by the user. Let θ represent the phase shift vector of the smart reflector. n This represents the phase shift of the nth element of the reflective surface, where N represents the total number of elements in the intelligent reflective surface.

[0017] Preferably, the optimization of the sum of all user rates involves the following steps:

[0018] Based on the established model of a smart reflector-assisted nonorthogonal multiple access system, the received signal-to-interference-plus-noise ratio γ of user k is... k Represented as:

[0019]

[0020] in Let μ(k) represent interference from other users, and let μ(k) represent the decoding order of user k. Since the user uses continuous interference cancellation technology at the receiver, it can be assumed that only signals decoded after user k will interfere with user k's signal. σ 2 This represents the thermal noise power received by the user.

[0021] Based on the signal-to-interference-plus-noise ratio (SINR), the information rate R of user k k Represented as:

[0022]

[0023] Where V(x) = 1 - (1 + x) -2 , m represents the number of channel implementations, and ε represents the decoding error probability.

[0024] The optimization problem is established with the sum of the information rates of all users as the optimization objective, while satisfying the constraint of the total transmit power of the base station, namely:

[0025]

[0026] in C1 represents the set of all possible decoding orders, C2 represents the case where the decoding order is constrained by channel state information, C3 represents the case where power allocation is constrained by the decoding order, and P represents the set of all possible decoding orders. max This is the maximum transmit power limit of the base station, w k This indicates the weight or priority of user k.

[0027] Preferably, the method for obtaining the optimal power allocation and intelligent reflector phase shift matrix for the optimization problem includes the following steps:

[0028] The possible decoding order is K!, meaning that as the number of users increases, traversing every possible decoding order becomes computationally expensive. Therefore, a relatively reliable scheme is proposed to determine the decoding order based on user priority and the distance between the user and the reflector. First, based on the distance between the user and the reflector, users farther away are decoded later, and those closer are decoded first. For users at the same distance, the order is determined by priority, with higher priority users decoded later.

[0029] Since the optimization problem is non-convex, it needs to be transformed into a convex problem. To this end, the power allocation subproblem and the reflection surface phase shift matrix optimization subproblem are obtained by alternately optimizing the two variables of intelligent reflector phase shift matrix and power allocation, respectively.

[0030] First, fix the power allocation {p k}, optimize the phase shift of the reflector. By transforming and rewriting the channel gain term, let but Then define and V = vvH Q k Let V represent the channel state information between user k, the reflector and the base station, and the phase shift information of the smart reflector, respectively, in Hermitian matrix form. V satisfies... That is, V is a positive semi-definite matrix with rank 1, diagonal elements of 1, and is a Hermitian matrix. The received signal-to-interference-plus-noise ratio (SINNR) of user k can be reconstructed using the trace of the matrix Tr(VQ). k )Express:

[0031]

[0032]

[0033] The objective function is the user information rate and It can be represented as:

[0034]

[0035] in

[0036]

[0037]

[0038] During the l-th iteration, V k (V) can be approximated by a first-order Taylor expansion as follows:

[0039]

[0040] Where V (l) This represents the feasible point in the l-th iteration. V represents k The derivative of (V). The above process transforms the subproblem of optimizing the phase shift V of the reflecting surface into a convex problem, that is:

[0041]

[0042] stC1:Tr(VQ k )≥Tr(VQ j If μ(k) > μ(j),

[0043] C2:rank(V)=1,

[0044] C3:[V] nn =1,n=1,2,...,N,

[0045] C4:

[0046] C5:

[0047] Then, with the phase shift V of the reflector fixed, the power allocation {p} is optimized. k The expressions for the signal-to-interference-plus-noise ratio (SINR) and the sum of user information rates are the same as those in (7) and (8). A slack variable t is introduced to constrain the lower bound of the objective function, transforming the objective function into a constraint condition. Then, a random variable α is introduced. k ≤γ k Constrain the lower bound of the signal-to-interference-plus-noise ratio, and use α k Replace user information rate and γ k So the problem becomes

[0048]

[0049] stC1:0≤p k ≤p j if μ(k)>μ(j)

[0050] C2:

[0051] C3:

[0052] C4:α k ≤γ k

[0053] Where C k (α k ) = log(1 + α k ),

[0054] During the l-th iteration, V k (α k Replace with a first-order Taylor approximation as

[0055]

[0056] in This is a feasible point in the l-th iteration. For V k (α k The derivative of ).

[0057] Introducing random variables The lower bound of the numerator of the signal-to-interference-plus-noise ratio expression, z k By constraining the upper bound of the denominator, we obtain the new constraint condition:

[0058]

[0059]

[0060]

[0061] Here, C4c is still a non-convex constraint; a convex constraint can be obtained using the first-order Taylor approximation.

[0062]

[0063] in This is an approximate point from the previous iteration.

[0064] The above process transforms the subproblem of optimizing power allocation into a convex problem, namely:

[0065]

[0066] In each iteration, the CVX toolbox was used to solve problems (12) and (19) respectively, and the optimal solutions for power and reflector were obtained using {p k} * and V * express.

[0067] Preferably, the method for obtaining the optimal intelligent reflector phase shift vector by Gaussian randomization of the optimal intelligent reflector phase shift matrix is ​​as follows:

[0068] For V * Perform singular value decomposition, i.e., V * =ASB, generates L random vectors r l And construct vectors make Find x that maximizes both the number of users and the speed. l This is the optimal intelligent reflective surface vector. The final output is the optimal solution that satisfies the iteration completion condition.

[0069] This application also provides a power allocation system for a smart reflector-assisted nonorthogonal multiple access system, comprising:

[0070] System model construction module: Based on the number and location of users and the number of smart reflector elements, a non-orthogonal multiple access system model of base station, smart reflector and ultra-reliable low latency users is established. The base station uses superimposed coding to send signals to users. When the direct link between the base station and the user is blocked, the signal from the base station is directly reflected by the smart reflector to add a reliable communication link to the user.

[0071] Power allocation calculation module: Based on the non-orthogonal multiple access system model, jointly design the base station transmit power allocation, intelligent reflector phase shift vector and decoding order. The optimal power allocation and intelligent reflector phase shift vector need to optimize the sum of all user rates.

[0072] The power allocation calculation module includes: a decoding order design submodule that designs the decoding order based on the distance and priority between the user and the reflector; a power optimization submodule that optimizes power allocation; and an intelligent reflector optimization submodule that optimizes the phase shift matrix of the intelligent reflector. The optimal power allocation and intelligent reflector phase shift matrix are obtained by alternately optimizing the power allocation and the intelligent reflector phase shift matrix.

[0073] Output module: Gaussian randomizes the optimal intelligent reflector phase shift matrix to obtain the corresponding intelligent reflector phase shift vector, and determines whether the condition for completing the iteration is met. If it is met, the iteration ends.

[0074] This application also provides a terminal device, including: a memory; one or more processors coupled to the memory; and one or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the one or more processors, and the one or more application programs are configured to perform a power allocation method for a smart reflector-assisted nonorthogonal multiple access system.

[0075] This application also provides a computer-readable storage medium containing program code that can be called by a processor to execute a power allocation method for a smart reflector-assisted nonorthogonal multiple access system.

[0076] Beneficial effects:

[0077] 1) The method of the present invention combines the advantages of intelligent reflective surface to enhance coverage and the advantages of non-orthogonal multiple access, thereby improving the overall rate performance and reliability of users and meeting users' needs for ultra-reliable low latency.

[0078] 2) The power allocation method for non-orthogonal multiple access systems designed in this invention can adjust the base station transmit power according to the user's channel state information and weight, which can improve system fairness and ensure the information rate performance of users with poor channel conditions;

[0079] 3) The power allocation method for non-orthogonal multiple access systems designed in this invention allows multiple users to use the same physical resource, thus achieving high bandwidth efficiency. Attached Figure Description

[0080] Figure 1 This is a model diagram of the intelligent reflective surface-assisted nonorthogonal multiple access system constructed according to the present invention.

[0081] Figure 2 This is a flowchart of the present invention.

[0082] Figure 3 This is a schematic diagram of the convergence curves of the present invention compared with other methods in the embodiments.

[0083] Figure 4This is a schematic diagram showing the number and rate of different smart reflective surface elements compared to other methods in this embodiment.

[0084] Figure 5 This is a schematic diagram showing different total transmit power and rate compared with other methods in the embodiments of the present invention. Detailed Implementation

[0085] The embodiments of the present invention will be further described below with reference to the accompanying drawings:

[0086] The present invention provides a power allocation method for a smart reflector-assisted nonorthogonal multiple access system, which specifically includes the following steps:

[0087] Step 1: Establish as follows Figure 1 The non-orthogonal multiple access system model shown includes a base station, a smart reflector, and ultra-reliable low-latency users. It assumes the base station is located at the origin of the coordinate axis, and users are randomly distributed within a circular area with a center and radius of 20m, 200m from the base station. The smart reflector is deployed at (150m, 30m). The base station transmits signals to users using superposition coding. When the direct link between the base station and users is blocked, the smart reflector directly reflects signals from the base station, providing users with a reliable communication link. Through non-orthogonal multiple access, the base station superimposes and encodes signals from each user, utilizing the same physical resource for transmission, thus improving bandwidth efficiency. Users utilize continuous interference cancellation to remove interference between users, increasing user data rate.

[0088] Assume the base station is configured with a single antenna, and the K ultra-reliable low latency (URLLC) users are also configured with single antennas. The base station's transmitted signal is:

[0089]

[0090] Where s k p represents the transmitted signal of user k. k This represents the transmit power of user k.

[0091] The signal received by user k is:

[0092]

[0093] Where g and z represents the channel state information between the base station and the smart reflector, and between the smart reflector and user k, respectively. k This represents the Gaussian white noise signal received by the user. This represents the phase shift vector of the intelligent reflective surface.

[0094] Step 2: Based on the intelligent reflector-assisted non-orthogonal multiple access system model in Step 1, in order to improve the sum rate of system users and meet the base station's transmit power constraints, jointly design the base station transmit power allocation, intelligent reflector phase shift vector and decoding order, and the optimal power allocation and intelligent reflector phase shift vector;

[0095] The specific steps to optimize the sum of user speeds are as follows:

[0096] Step 2.1: Based on the intelligent reflector-assisted non-orthogonal multiple access system model established in Step 1, the user's received signal-to-interference-plus-noise ratio (SIR) is:

[0097]

[0098] in Let μ(k) represent interference from other users, and let μ(k) represent the decoding order of user k. Since the user uses continuous interference cancellation technology at the receiver, it can be assumed that only signals decoded after user k will interfere with user k's signal. σ 2 This represents the thermal noise power received by the user.

[0099] Based on the signal-to-interference-plus-noise ratio (SINR), the information rate R of user k k Represented as:

[0100]

[0101] Where V(x) = 1 - (1 + x) -2 , m represents the number of channel implementations, and ε represents the decoding error probability. In the experiment, the number of channel implementations m = 200, and the decoding error probability was set to 10e-6.

[0102] Step 2.2: Using the sum of information rates of all users as the optimization objective, establish an optimization problem that satisfies the constraint of the total transmit power of the base station, namely:

[0103]

[0104] in C1 represents the set of all possible decoding orders, C2 represents the case where the decoding order is constrained by channel state information, C3 represents the case where power allocation is constrained by the decoding order, and P represents the set of all possible decoding orders. max This is the maximum transmit power limit of the base station, w k This indicates the weight or priority of user k.

[0105] Step 3: The power allocation problem that requires optimizing the sum of the rates of all users while eliminating mutual interference between users is transformed into a convex problem that is easy to solve. Specifically, the decoding order is designed according to the distance and priority between the user and the reflector. The optimal power allocation and intelligent reflector phase shift matrix are obtained by alternately optimizing the power allocation and intelligent reflector phase shift matrix, and it is determined whether the conditions for the iteration to be completed are met.

[0106] The specific methods for obtaining optimal power allocation and intelligent reflector vector are as follows:

[0107] Step 3.1: The possible decoding order is K!, meaning that as the number of users increases, traversing every possible decoding order becomes computationally expensive. Therefore, a relatively reliable scheme is proposed to determine the decoding order based on user priority and the distance between the user and the reflector. First, based on the distance between the user and the reflector, users further away are decoded later, and those closer are decoded first. For users at the same distance, the order is determined by priority, with higher priority users decoded later.

[0108] Step 3.2: Since the optimization problem in Step 2.2 is non-convex, we need to transform it into a convex problem. To do this, we obtain the power allocation subproblem and the reflection surface phase shift matrix optimization subproblem by alternately optimizing the two variables of intelligent reflector phase shift matrix and power allocation, respectively.

[0109] First, fix the power allocation {p k}, optimize the phase shift of the reflector. By transforming and rewriting the channel gain term, let but Then define and V = vv H Q k Let V represent the channel state information between user k, the reflector and the base station, and the phase shift information of the smart reflector, respectively, in Hermitian matrix form. V satisfies... That is, V is a positive semi-definite matrix with rank 1, diagonal elements of 1, and is a Hermitian matrix. The received signal-to-interference-plus-noise ratio (SINNR) of user k can be reconstructed using the trace of the matrix Tr(VQ). k )Express:

[0110]

[0111]

[0112] The objective function is the user information rate and It can be represented as:

[0113]

[0114] in

[0115]

[0116]

[0117] During the l-th iteration, V k (V) can be approximated by a first-order Taylor expansion as follows:

[0118]

[0119] Where V (l) This represents the feasible point in the l-th iteration. V represents k The derivative of (V). The above process transforms the subproblem of optimizing the phase shift V of the reflecting surface into a convex problem, that is:

[0120]

[0121] stC1:Tr(VQ k )≥Tr(VQ j If μ(k) > μ(j),

[0122] C2:rank(V)=1,

[0123] C3:[V] nn =1,n=1,2,...,N,

[0124] C4:

[0125] C5:

[0126] Then, with the phase shift V of the reflector fixed, the power allocation {p} is optimized. k The expressions for the signal-to-interference-plus-noise ratio (SINR) and the sum of user information rates are the same as those in (7) and (8). A slack variable t is introduced to constrain the lower bound of the objective function, transforming the objective function into a constraint condition. Then, a random variable α is introduced. k ≤γ k Constrain the lower bound of the signal-to-interference-plus-noise ratio, and use α k Replace user information rate and γ k So the problem becomes

[0127]

[0128] stC1:0≤p k ≤p j if μ(k)>μ(j)

[0129] C2:

[0130] C3:

[0131] C4:α k ≤γ k

[0132] Where C k (α k ) = log(1 + α k ),

[0133] During the l-th iteration, V k (α k Replace with a first-order Taylor approximation as

[0134]

[0135] in This is a feasible point in the l-th iteration. For V k (α k The derivative of ).

[0136] Introducing random variables The lower bound of the numerator of the signal-to-interference-plus-noise ratio expression, z k By constraining the upper bound of the denominator, we obtain the new constraint condition:

[0137]

[0138]

[0139]

[0140] Here, C4c is still a non-convex constraint; a convex constraint can be obtained using the first-order Taylor approximation.

[0141]

[0142] in This is an approximate point from the previous iteration.

[0143] The above process transforms the subproblem of optimizing power allocation into a convex problem, namely:

[0144]

[0145] Step 3.3: In each iteration, use the CVX toolbox to solve problems (12) and (19) respectively, and finally obtain the optimal solutions for power and reflector surface using {p k} * and V * express.

[0146] Step 4: Use Gaussian randomization to make the optimal intelligent reflector phase shift matrix obtained in Step 3 satisfy the constraint that the matrix rank is 1, obtain the corresponding intelligent reflector phase shift vector, and determine whether the condition for completing the iteration is met. If it is met, the iteration ends.

[0147] For V * Perform singular value decomposition, i.e., V * =ASB, generates L random vectors r l And construct vectors make Find x that maximizes both the number of users and the speed. l This is the optimal intelligent reflective surface vector. The final output is the optimal solution that satisfies the iteration completion condition.

[0148] In the experiment, to verify the performance of the designed algorithm, it was assumed that the channel link assisted by the intelligent reflector follows a Ricean distribution, the Ricean factor was set to 10, the noise power spectral density was -174 dBm / Hz, the transmission bandwidth was 180 kHz, the error probability was set to 10e-6, and the number of channel implementations was set to 200. Finally, the algorithm designed in this invention was compared with other benchmark algorithms, including schemes that use the Shannon formula to calculate the information rate (Shannon), schemes that use random phase distribution and independent optimized power allocation (Random phase), schemes that use random power allocation and independent optimized intelligent reflector phase shift (IRS), and schemes that use frequency division multiple access with optimized power and intelligent reflector phase shift (FDMA). The results showed that the algorithm of this invention can converge relatively quickly and achieve a higher sum rate.

[0149] This application also provides a power allocation system for a non-orthogonal multiple access system assisted by a smart reflector, which mainly includes: a system model construction module: based on the number and location of users and the number of smart reflector elements, a non-orthogonal multiple access system model of a base station, a smart reflector and an ultra-reliable low-latency user is established. The base station sends signals to the user using superposition coding. When the direct link between the base station and the user is blocked, the signal from the base station is directly reflected by the smart reflector to add a reliable communication link to the user.

[0150] Power allocation calculation module: Based on the non-orthogonal multiple access system model, jointly design the base station transmit power allocation, intelligent reflector phase shift vector and decoding order. The optimal power allocation and intelligent reflector phase shift vector need to optimize the sum of all user rates.

[0151] The power allocation calculation module includes: a decoding order design submodule that designs the decoding order based on the distance and priority between the user and the reflector; a power optimization submodule that optimizes power allocation; and an intelligent reflector optimization submodule that optimizes the phase shift matrix of the intelligent reflector. The optimal power allocation and intelligent reflector phase shift matrix are obtained by alternately optimizing the power allocation and the intelligent reflector phase shift matrix.

[0152] Output module: Gaussian randomizes the optimal intelligent reflector phase shift matrix to obtain the corresponding intelligent reflector phase shift vector, and determines whether the condition for completing the iteration is met. If it is met, the iteration ends.

[0153] Specific limitations regarding the power allocation system for non-orthogonal multiple access systems can be found in the above description of the power allocation method for intelligent reflector-assisted non-orthogonal multiple access systems, and will not be repeated here. Each module in the aforementioned device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware or independently of the processor in the terminal device, or stored in software in the memory of the terminal device, so that the processor can call and execute the corresponding operations of each module.

[0154] This application also provides a terminal device, which may be a computer device. The terminal device in this application may include one or more of the following components: a processor, a memory, and one or more application programs. The one or more application programs may be stored in the memory and configured to be executed by one or more processors. The one or more application programs are configured to execute the methods described in the above-described method embodiments applied to the terminal device, and may also be configured to execute the above-described power allocation method for a smart reflector-assisted non-orthogonal multiple access system.

[0155] A processor may include one or more processing cores. The processor connects to various parts of the terminal device using various interfaces and lines, and performs various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory, and by calling data stored in memory. Optionally, the processor may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the displayed content; and the modem handles wireless communication. It is understood that the modem may also be implemented separately as a communication chip, without being integrated into the processor.

[0156] The memory may include random access memory (RAM) or read-only memory (ROM). The memory can be used to store instructions, programs, code, code sets, or instruction sets. The memory may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as touch functionality, sound playback functionality, image playback functionality, etc.), and instructions for implementing the various method embodiments described below. The data storage area may also store data created by the terminal device during use.

[0157] This application also provides a computer-readable storage medium. The computer-readable storage medium stores program code that can be called by a processor to execute the aforementioned power allocation method for a smart reflector-assisted non-orthogonal multiple access system.

[0158] Computer-readable storage media can be electronic storage devices such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Optionally, computer-readable storage media include non-transitory computer-readable storage media. The computer-readable storage medium has storage space for program code that performs any of the method steps described above. This program code can be read from or written to one or more computer program products. The program code can be compressed, for example, in a suitable form.

[0159] It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention. All components not explicitly stated in this embodiment can be implemented using existing technology.

Claims

1. A power allocation method for a smart reflector-assisted nonorthogonal multiple access system, characterized in that, The specific steps are as follows: A non-orthogonal multiple access system model is established, consisting of a base station, a smart reflector, and ultra-reliable low-latency users. The base station uses superposition coding to send signals to users. When the direct link between the base station and the user is blocked, the smart reflector directly reflects the signal from the base station to add a reliable communication link for the user. Based on the non-orthogonal multiple access system model, the base station transmit power allocation, intelligent reflector phase shift vector, and decoding order are jointly designed. The optimal power allocation and intelligent reflector phase shift vector require optimization of the sum of all user rates. The decoding order is designed based on the distance and priority between the user and the reflector, and the power allocation and intelligent reflector phase shift matrix are optimized alternately. Specifically, this includes obtaining the sub-problem of optimizing the reflector phase shift matrix and transforming it into a convex problem for solution, thereby obtaining the optimal power allocation and intelligent reflector phase shift matrix. Gaussian randomization is performed on the optimal intelligent reflector phase shift matrix to obtain the corresponding intelligent reflector phase shift vector, and it is determined whether the condition for completing the iteration is met. If the condition is met, the iteration ends. The specific steps involved in obtaining the subproblem of optimizing the phase shift matrix of the reflecting surface and transforming it into a convex problem include: Fixed power distribution Optimize the phase shift of the reflector; rewrite the channel gain term by transformation, making... , ,but Then define and , and Representing users in Hermitian matrix form Channel state information between the base station and the smart reflector, and phase shift information of the smart reflector; satisfy That is, satisfying It is a positive semi-definite matrix with a rank of 1 and diagonal elements of 1. It is a Hermitian matrix; user The received signal-to-interference-plus-noise ratio is reconstituted in the form of the trace of the matrix. Express: (6)； (7)； The objective function is the user information rate and Represented as: (8)； in (9)； (10)； In the During the next iteration, Approximated by a first-order Taylor expansion: : (11)； in Indicates the first Feasible point in the next iteration express The derivative; the above process will optimize the phase shift of the reflecting surface. The subproblem is transformed into a convex problem, that is: (12)； C4: V ≥ 0; ； in and These respectively represent the relationship between the base station and the smart reflector, and between the smart reflector and the user. Channel vectors between Indicates user The transmission power, Indicates the first reflecting surface Phase shift of each element, This represents the total number of smart reflective surface elements. Indicates user The received signal-to-interference-plus-noise ratio, Indicates user Information rate Indicates user The index value in the decoding order. The thermal noise power received by the user is represented by m, which represents the number of channel implementations. C1 represents the decoding error probability, C2 represents the constraint of the decoding order on the channel state information, and C3 represents the constraint of the power allocation on the decoding order. Indicates user Weight or priority.

2. The power allocation method for a non-orthogonal multiple access system assisted by a smart reflector as described in claim 1, characterized in that, The specific model of the non-orthogonal multiple access system is as follows: Assume the base station is configured with a single antenna. Each ultra-reliable low-latency user has a single antenna, and the base station's transmitted signal is: (1)； in Indicates user The transmitted signal, Indicates user The transmission power; user The received signal is: (2)； in and These respectively represent the relationship between the base station and the smart reflector, and between the smart reflector and the user. Channel vectors between This represents the Gaussian white noise signal received by the user. Denotes the phase shift vector of the smart reflector, where Indicates the first reflecting surface Phase shift of each element, This represents the total number of smart reflective surface elements.

3. The power allocation method for a non-orthogonal multiple access system assisted by a smart reflector according to claim 2, characterized in that, The specific steps for optimizing the sum of rates for all users are as follows: Based on the established model of a smart reflector-assisted nonorthogonal multiple access system, the user Received signal-to-interference-plus-noise ratio Represented as: (3)； in This indicates interference from other users. Indicates user The index value in the decoding order. This indicates the thermal noise power received by the user; Based on the signal-to-interference-plus-noise ratio, the user Information rate Represented as: (4)； in , m represents the number of channel implementations. Indicates the probability of decoding errors; The optimization problem is established with the sum of the information rates of all users as the optimization objective, while satisfying the constraint of the total transmit power of the base station, namely: (5)； in D1 represents the set of all possible decoding orders, D2 represents the case where the decoding order is constrained by channel state information, and D3 represents the case where power allocation is constrained by the decoding order. It is the maximum transmit power limit of the base station. Indicates user Weight or priority.

4. The power allocation method for a nonorthogonal multiple access system assisted by a smart reflector according to claim 3, characterized in that, The specific steps for obtaining the optimal power allocation and the phase shift matrix of the intelligent reflector are as follows: Based on the distance between the user and the reflective surface, those that are farther away are decoded later, and those that are closer are decoded first. If the distances are the same, the higher priority is determined by the order of decoding. By alternately optimizing the two variables of the intelligent reflector phase shift matrix and power allocation, the subproblem of power allocation and the subproblem of the reflector phase shift matrix optimization are obtained respectively, and the subproblem of power allocation and the subproblem of the reflector phase shift matrix optimization are transformed into convex problems respectively; In each iteration, the two convex problems are solved using the CVX toolbox, yielding the optimal solutions for power and the reflecting surface. and express.

5. The power allocation method for a non-orthogonal multiple access system assisted by a smart reflector according to claim 3, characterized in that, The specific steps to obtain the power allocation subproblem and transform it into a convex problem include: Fixed reflector phase shift Optimize power distribution The expressions for the signal-to-interference-plus-noise ratio and the sum of user information rates are the same as those in formulas (7) and (8); a slack variable t is introduced to constrain the lower bound of the objective function, and the objective function is transformed into a constraint condition; then a random variable is introduced. Constrain the lower bound of the signal-to-interference-plus-noise ratio, and use... Replace user information rate and So the problem becomes ； (13)； in , ; In the During the next iteration, Replace with a first-order Taylor approximation. : (14)； in For the first In the next iteration The current value, for about The derivative; Introducing random variables Constrain the lower bound of the numerator of the signal-to-interference-plus-noise ratio expression. By constraining the upper bound of the denominator, we obtain new constraints: (15)； (16)； (17)； C4c remains a non-convex constraint; a convex constraint can be obtained using the first-order Taylor approximation. (18)； in , In the previous iteration , The current value; The above process transforms the subproblem of optimizing power allocation into a convex problem, namely: ； (19)。 6. The power allocation method for a non-orthogonal multiple access system assisted by a smart reflector according to claim 4, characterized in that, The method described above for obtaining the corresponding smart reflector phase shift vector by Gaussian randomizing the optimal smart reflector phase shift matrix is ​​as follows: right Perform singular value decomposition, i.e. ,produce random vectors And construct vectors ,make Find the one that maximizes user and speed This is the corresponding phase shift vector of the intelligent reflector, and the final output is the optimal solution that satisfies the iteration completion condition.

7. A power allocation system for a smart reflector-assisted nonorthogonal multiple access system, used to implement the method described in any one of claims 1-6, characterized in that, include: System model construction module: Based on the number and location of users and the number of smart reflector elements, a non-orthogonal multiple access system model of base station, smart reflector and ultra-reliable low latency users is established. The base station uses superimposed coding to send signals to users. When the direct link between the base station and the user is blocked, the signal from the base station is directly reflected by the smart reflector to add a reliable communication link to the user. Power allocation calculation module: Based on the non-orthogonal multiple access system model, jointly design the base station transmit power allocation, intelligent reflector phase shift vector and decoding order. The optimal power allocation and intelligent reflector phase shift vector need to optimize the sum of all user rates. The power allocation calculation module includes: a decoding order design submodule that designs the decoding order based on the distance and priority between the user and the reflector; a power optimization submodule that optimizes power allocation; and an intelligent reflector optimization submodule that optimizes the phase shift matrix of the intelligent reflector. The optimal power allocation and intelligent reflector phase shift matrix are obtained by alternately optimizing the power allocation and the intelligent reflector phase shift matrix. Output module: Gaussian randomizes the optimal intelligent reflector phase shift matrix to obtain the corresponding intelligent reflector phase shift vector, and determines whether the condition for completing the iteration is met. If it is met, the iteration ends.

8. A terminal device, characterized in that, include: Memory; One or more processors are coupled to the memory; One or more applications, wherein the one or more applications are stored in memory and configured to be executed by one or more processors, and the one or more applications are configured to perform the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program code that can be invoked by a processor to execute the method as described in any one of claims 1-6.

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