User grouping and beamforming method for a cyber-physical coordinated transmission system

Abstract

This invention discloses a user grouping and beamforming method for a data and energy cooperative transmission system, belonging to the field of wireless data and energy cooperative transmission. The method includes the following steps: determining the near-field channel model of a circular holographic MIMO antenna array; determining the holographic MIMO transmitter model; determining the user grouping scheme and adopting the corresponding transmission scheme according to the user grouping situation; determining the holographic MIMO data and energy reception model; constructing a joint optimization problem with system efficiency and fairness rate as objectives; designing a joint beamforming and power allocation algorithm based on alternating optimization and fractional programming; and adjusting the configuration through simulation. This invention can achieve user grouping by combining near-field channel resolution characteristics, and through the joint optimization of beamforming and power allocation, it can significantly improve the system fairness rate while meeting energy harvesting requirements.

Description

Technical Field

[0001] This invention belongs to the field of wireless data and energy coordinated transmission, and specifically relates to a user grouping and beamforming method for a data and energy coordinated transmission system. Background Technology

[0002] Against the backdrop of accelerated informatization and intelligentization, wireless communication technology has become a critical infrastructure of modern society. The rapid development of emerging applications such as the Internet of Things (IoT), 5G / 6G networks, intelligent transportation systems, and wearable devices has placed higher demands on the performance of communication systems. However, energy supply issues are gradually becoming a major bottleneck restricting the development of related scenarios. With the large-scale deployment of low-power IoT devices, these devices are constrained by strict battery capacity limitations, facing the dual pressures of energy replenishment and remote communication. In recent years, Integrated Data and Energy Transfer (IDET) technology, which integrates wireless data transmission and wireless energy transfer functions, has provided an effective way to solve the aforementioned energy and communication coordination problems.

[0003] With the increase in antenna size and operating frequency, near-field effects are becoming increasingly significant in multi-user holographic MIMO systems. In the near-field region, electromagnetic waves exhibit spherical wave characteristics, resulting in weak orthogonality of inter-user channels in the range domain, which limits the performance of traditional space-division multiple access (SDMA) and orthogonal multiple access (OMA) schemes. Meanwhile, the large-scale deployment of low-power IoT devices makes energy harvesting a crucial constraint in system design. Existing research largely focuses on beamforming design under far-field channel models, failing to fully consider the non-orthogonal characteristics of near-field channels and multi-user packet transmission, and lacking joint optimization of user channel correlation, energy requirements, and beamforming resources within the framework of simultaneous data and energy transmission. Summary of the Invention

[0004] To address the problems mentioned in the background art, this invention provides a user grouping and beamforming method for a data-energy coordinated transmission system, thereby solving the problems of low multi-user transmission efficiency and insufficient data and energy coordination design in the prior art under holographic MIMO near-field non-orthogonal channels.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A user packet and beamforming method for a data-powered collaborative transmission system includes the following steps:

[0007] S1: Determine the near-field channel model of the circular holographic MIMO antenna array; the transmitter of the near-field channel model adopts a uniform circular antenna array, establishes the geometric relationship between the user position and the antenna element in the spherical coordinate system, and derives the near-field channel vector h based on the line-of-sight dominant spherical wave model, and introduces a resolution function to quantize the user position;

[0008] S2: Determine the holographic MIMO transmitter model, which includes a digital beamformer, an RF link, and an analog beamformer, as well as a hybrid beamforming architecture consisting of an analog holographic beamforming matrix, a propagation matrix, and a baseband digital beamforming matrix.

[0009] S3: Users include data users and energy users. Data users are grouped based on the value quantized by the resolution function. NoMA transmission is used within the group and SDMA transmission is used between groups. The expression of the transmitted signal of the data user is determined based on the power allocation coefficient of the data user.

[0010] S4: Based on the near-field channel model, transmitter model, packet configuration, and transmitted signal expression, construct the data user received signal model. Further construct the energy user's energy harvesting value expression based on this model. Considering the nonlinear response of the rectifier circuit, establish the relationship between the energy user's energy harvesting value and the output DC power E. DC Relational model;

[0011] S5: Based on the data involved in S1-S4, construct a joint optimization problem with the goal of maximizing the minimum data user rate, and set constraints including data user transmit power and energy user energy harvesting; establish optimization models for power allocation coefficients, digital beamforming matrices and analog beamforming matrices;

[0012] S6: Design a joint beamforming and power allocation algorithm based on alternating optimization and fractional programming, decomposing the joint optimization problem into three sub-problems about the analog beamformer, the digital beamformer, and the power allocation coefficients for iterative solution;

[0013] S7: Based on the solution, compare the performance of different preset transmission schemes under different channel resolutions and user space distributions. Under the conditions of transmit power and energy harvesting requirements, evaluate the performance gain of the grouping scheme and joint optimization algorithm and make configuration adjustments.

[0014] Compared with the prior art, the beneficial effects of the present invention are:

[0015] This application applies to holographic MIMO near-field data-energy coordinated transmission systems. By quantifying and classifying user channel correlations, it achieves an efficient hybrid multiple access scheme that employs non-orthogonal multiple access transmission within groups and spatial division multiple access transmission between groups. The alternating optimization algorithm designed in this application can efficiently solve non-convex joint optimization problems, realizing the coordinated design of beamforming and power allocation. Finally, system simulation verifies the performance advantages of the proposed scheme under different user distributions, transmit power, and energy harvesting requirements, revealing the intrinsic correlation between channel resolution threshold and system performance. It provides practical guidance for parameter configuration and resource scheduling of near-field holographic MIMO systems, improving the transmission efficiency of multiple users under holographic MIMO near-field non-orthogonal channels. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the process of this application;

[0017] Figure 2 This is a schematic diagram of the model structure of this application. Detailed Implementation

[0018] To facilitate understanding of the technical content of this invention by those skilled in the art, the invention will be further described in detail below with reference to the accompanying drawings and specific examples. It should be understood that the specific examples described herein are merely illustrative and not intended to limit the scope of the invention.

[0019] A user packet and beamforming method for a data-powered collaborative transmission system, such as Figure 1 As shown, it includes the following steps:

[0020] S1: Determine the near-field channel model of the circular holographic MIMO antenna array: Using a circular antenna array, the user's position in the spherical coordinate system is... In near-field communication scenarios, line-of-sight channels are the dominant channel type (multipath effects are usually negligible); assuming a user is equipped with only a single antenna, the mathematical characterization of the near-field channel can be described as follows:

[0021] ;

[0022] Where N represents the number of transmit antennas, and α represents the complex path gain of the line-of-sight (LoS) link. The array response vector in spherical coordinates is specifically represented as:

[0023] ;

[0024] Where λ is the wavelength of the electromagnetic wave in free space. The distance between the user and the nth antenna is specifically expressed as:

[0025] ;

[0026] Where R is the radius of the circular antenna array. Let be the azimuth angle of the nth antenna element;

[0027] To further quantify the model's ability to distinguish users at different locations in space, a resolution function needs to be introduced as a core analytical tool, which is defined as:

[0028] .

[0029] S2: Determine the holographic MIMO transmitter model: The transmitter model is as follows Figure 2As shown, the hybrid beamforming architecture comprises an analog holographic beamforming matrix, a propagation matrix, and a baseband digital beamforming matrix. The baseband digital beamforming performs digital preprocessing of multi-user signals on the RF link; the propagation matrix characterizes the waveguide transmission characteristics from the feed to the antenna elements; and the analog holographic beamforming achieves spatial modulation of electromagnetic wave amplitude and phase through metamaterial surfaces, with the response of each element constrained by amplitude limitations.

[0030] S3: Determine the user grouping scheme and adopt the corresponding transmission scheme based on the user grouping situation: Consider a downlink network with K data users and L energy users, by U k U i This means that K data users are grouped according to the value of a resolution function. Users with a resolution greater than a certain threshold are grouped into the same group. Users within the same group have strong channel correlation, while users in different groups have weak channel correlation. Let's assume there are M user groups, each using NOMA transmission, and SDMA transmission between different groups.

[0031] Suppose the signal is sent Each S m The data user superimposed signal of the m-th group, multiplied by the power allocation coefficient, can be expressed as:

[0032] ;

[0033] Among them, G m p is the set of data users in the m-th group. m,i x is the power allocation coefficient for data user i. m,i It is the signal of data user i, and it follows a complex Gaussian distribution.

[0034] S4: Determine the holographic MIMO data and energy reception model: The received signal for data user i is:

[0035] ;

[0036] Among them, w m b is the beamforming vector of the m-th group. m For baseband digital beamformer The m-th column, For H-MIMO analog beamformers, Let N be the propagation matrix, and N be the number of transmitting antennas. RF The number of radio frequency links (RF chains);

[0037] There are usually three types:

[0038] •Amplitude only, 0 < φ < 1;

[0039] • Binary amplitude, i.e., φ=0 or 1;

[0040] • Lorentz phase constraint, i.e. .

[0041] Users within a group use NOMA transmission with the same decoding operation, while users between groups use SDMA transmission. In this case, the signal-to-interference-plus-noise ratio (SINR) of the i-th data user can be expressed as:

[0042] ;

[0043] The reachable rate of the i-th data user can be expressed as: ;

[0044] Meanwhile, the energy collection value of the l-th energy user can be expressed as:

[0045] ;

[0046] Considering the nonlinear response of the rectifier circuit, the received RF power E RF With output DC power E DC The relationship can be modeled as follows:

[0047] ;

[0048] in, , Parameters a and b represent influencing factors such as capacitance, resistance, and sensitivity of the rectifier circuit, E sat This represents the saturated output power.

[0049] S5: Constructing a joint optimization problem with system fairness rate as the objective: By jointly optimizing the power allocation coefficient, the baseband digital beamformer, and the analog beamformer, our goal is to maximize the minimum rate for data users to ensure fairness among users, while simultaneously meeting the energy harvesting needs of energy users. The optimization problem can be expressed as:

[0050] ;

[0051] This indicates that the power of the transmitter does not exceed the total power, meets the decoding sequence requirements of serial interference cancellation (SIC), and the beamformer meets the analog domain constraints of the holographic metasurface and the energy harvesting requirements.

[0052] S6. Design of a joint beamforming and power allocation algorithm based on alternating optimization and fractional programming: An alternating optimization algorithm is studied to solve the above problems. The optimization objective is achieved by iteratively solving the analog beamformer, digital beamformer, and power allocation coefficients, specifically divided into the following three sub-problems.

[0053] 1. Simulated beamformer:

[0054] For max-min fraction optimization problems involving multiple users, auxiliary variables can be introduced. The original problem is equivalently transformed into the following form:

[0055] ;

[0056] The design of beamformers is essentially a multi-objective non-convex optimization problem, and its challenges mainly come from two aspects: First, the rate R m,i Contains non-convex terms Secondly, the system's energy constraints also exhibit non-convex characteristics. When solving this subproblem, both the propagation matrix P and the digital beamforming matrix B are known parameters. To simplify the expression, we define... This represents the equivalent beam vector of the corresponding user group. Simultaneously, the power allocation coefficients for each group are also known, and are defined as follows: , representing the sum of the power of all users after the current user i after the execution of SIC in the m-th group; further defined , representing the total transmit power of the other groups j.

[0057] To effectively solve this problem, a fractional programming method is introduced to address the rate R. m,i Perform an equivalent transformation and introduce an auxiliary variable y. m,i Using a quadratic transformation:

[0058] ;

[0059] The energy harvesting constraint also exhibits non-convex characteristics and can be equivalently transformed into:

[0060] ;

[0061] Since all parameters on the right-hand side of the inequality are known quantities, they can be uniformly defined as a constant τ. And for E... RF The Continuous Convex Approximation (SCA) method is used for processing. Let the current point be... For E RF Performing a first-order Taylor expansion, we obtain the linear convex constraint as follows:

[0062] ;

[0063] 2. Digital beamformer:

[0064] ;

[0065] The scheme is largely the same as the one mentioned above, with the main difference being that its optimization variable is the digital precoding matrix B, and it does not have the constraint of the analog domain.

[0066] 3. Power distribution factor:

[0067] ;

[0068] Since all constraints are convex, only the fractional terms in the objective function need to be processed. This can be achieved by using the same quadratic transformation method described above to convexize the rate expression.

[0069] In this subproblem, both the analog beamformer Φ and the digital beamformer B are fixed. For simplicity, we define... Let represent the squared effective signal gain of the i-th user in the m-th group. Meanwhile, for interference terms from other groups j, it is defined as... . Let represent the total transmission power of the other groups j. Using a quadratic transformation, the final constraint is:

[0070] .

[0071] S7. Simulations were used to verify the impact of near-field resolution and user distribution on the rate of different transmission schemes, and reasonable thresholds were set accordingly. Performance comparisons of traditional non-orthogonal multiple access (NOMA) and space-division multiple access (SDMA) schemes were conducted under different channel correlation environments and user spatial distribution scenarios, providing important basis for resolution threshold-based grouping strategies. NOMA transmission is recommended within highly correlated groups to improve system robustness and fairness. Simulations evaluated the overall performance of the proposed grouping scheme in terms of system achievable rate, user fairness, and energy efficiency. The impact of transmit power, user distribution patterns, and energy harvesting requirements on the rate of the three transmission schemes was analyzed, verifying the adaptability of the proposed scheme in different scenarios.

[0072] Those skilled in the art should understand that the above embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention. Any reasonable modifications, equivalent substitutions, or adaptive improvements made based on the technical concepts disclosed in this invention without departing from the essential spirit of the invention should be considered to fall within the scope of protection defined by the claims of this invention.

Claims

1. A user packetization and beamforming method for a data-powered collaborative transmission system, characterized in that, Includes the following steps: S1: Determine the near-field channel model of the circular holographic MIMO antenna array; the transmitter of the near-field channel model adopts a uniform circular antenna array, establishes the geometric relationship between the user position and the antenna element in the spherical coordinate system, and derives the near-field channel vector h based on the line-of-sight dominant spherical wave model, and introduces a resolution function to quantize the user position; S2: Determine the holographic MIMO transmitter model, which includes a digital beamformer, an RF link, and an analog beamformer, as well as a hybrid beamforming architecture consisting of an analog holographic beamforming matrix, a propagation matrix, and a baseband digital beamforming matrix. S3: Users include data users and energy users. Data users are grouped based on the value quantized by the resolution function. NoMA transmission is used within the group and SDMA transmission is used between groups. The expression of the transmitted signal of the data user is determined based on the power allocation coefficient of the data user. S4: Based on the near-field channel model, transmitter model, packet configuration, and transmitted signal expression, construct the data user received signal model. Further construct the energy user's energy harvesting value expression based on this model. Considering the nonlinear response of the rectifier circuit, establish the relationship between the energy user's energy harvesting value and the output DC power E. DC Relational model; S5: Based on the data involved in S1-S4, construct a joint optimization problem with the goal of maximizing the minimum data user rate, and set constraints including data user transmit power and energy user energy harvesting. Establish optimization models for power allocation coefficients, digital beamforming matrices, and analog beamforming matrices; S6: Design a joint beamforming and power allocation algorithm based on alternating optimization and fractional programming, decomposing the joint optimization problem into three sub-problems about the analog beamformer, the digital beamformer, and the power allocation coefficients for iterative solution; S7: Based on the solution, compare the performance of different preset transmission schemes under different channel resolutions and user space distributions. Under the conditions of transmit power and energy harvesting requirements, evaluate the performance gain of the grouping scheme and joint optimization algorithm and make configuration adjustments.

2. The user grouping and beamforming method for a data-energy collaborative transmission system according to claim 1, characterized in that, In S1, the user's position in the spherical coordinate system is... If a communication user is equipped with only a single antenna, then the mathematical representation of the near-field channel is as follows: ； Where N represents the number of transmitting antennas, and α represents the complex path gain of the line-of-sight link. The complex path gain of the line-of-sight channel is specifically expressed as: ； Where λ is the wavelength of the electromagnetic wave in free space. The distance between the user and the nth antenna is specifically expressed as: ； Where R is the radius of the circular antenna array. Let be the azimuth angle of the nth antenna element; The resolution function is defined as: 。 3. The user grouping and beamforming method for a data-energy cooperative transmission system according to claim 2, characterized in that, The baseband digital beamforming matrix is ​​used to perform digital preprocessing of multi-user signals on the radio frequency link; the propagation matrix is ​​used to characterize the waveguide transmission characteristics from the feed to the antenna element; the analog holographic beamforming matrix realizes spatial modulation of electromagnetic wave amplitude and phase through the metamaterial surface, and the response of each array element is limited by amplitude constraints.

4. The user grouping and beamforming method for a data-energy cooperative transmission system according to claim 3, characterized in that, In S3, there are K data users and L energy users. The K data users are grouped according to the value of a resolution function, with data users exceeding a preset resolution threshold grouped into the same group, resulting in a total of M user groups. The transmitted signal is set to... S m The signal superimposed on the data user of the m-th group is represented as: ； Among them, G m p is the set of data users in the m-th group. m,i x is the power allocation coefficient for data user i. m,i It is the signal of data user i, and it follows a complex Gaussian distribution.

5. The user grouping and beamforming method for a data-energy cooperative transmission system according to claim 4, characterized in that, In S4, the received signal of data user i is represented as: ； Among them, w m b is the beamforming vector of the m-th group. m For baseband digital beamformer The m-th column, For H-MIMO analog beamformers, Let N be the propagation matrix, and N be the number of transmitting antennas. RF The number of radio frequency links; ; Φ has three types, namely: Amplitude only: 0 < φ < 1; Binary amplitude: φ = 0 or 1; Lorentz phase constraint: ; The energy collection value of the l-th energy user is represented as: ； E RF With output DC power E DC The relationship is modeled as follows: ； in, , Parameters a and b are the influencing factors of the rectifier circuit, E sat This represents the saturated output power.

6. The user grouping and beamforming method for a data-powered collaborative transmission system according to claim 5, characterized in that, In S5, the optimization problem and constraints are specifically represented as follows: 。 7. The user grouping and beamforming method for a data-energy cooperative transmission system according to claim 6, characterized in that, The optimization problem and constraints of the simulated beamformer are specifically expressed as follows: 。 8. The user grouping and beamforming method for a data-energy cooperative transmission system according to claim 7, characterized in that, The optimization problem and constraints of digital beamformers are specifically expressed as follows: 。 9. A user grouping and beamforming method for a data-energy cooperative transmission system according to claim 8, characterized in that, The optimization problem and constraints of the power allocation coefficient are specifically expressed as follows: 。

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