Method and apparatus for resource allocation in stream antenna assisted non-orthogonal multiple access system

Abstract

This invention discloses a method and apparatus for resource allocation in a non-orthogonal multiple access system assisted by a fluid antenna, relating to the fields of wireless communication and power transmission technology. The method includes: S1 constructing a non-orthogonal multiple access system assisted by a fluid antenna; S2 determining a transmission channel model; S3 constructing an objective function; and S4 analyzing the objective function based on a genetic algorithm to obtain power allocation results. The introduction of a fluid antenna and optimization of the non-orthogonal multiple access system, maximum ratio combining, and power allocation coefficients significantly improve the overall performance of the non-orthogonal multiple access system. The spatial freedom provided by the fluid antenna effectively enhances channel quality, resulting in a significantly lower overall system outage probability compared to traditional fixed antenna systems, while also ensuring fairness between the two users in the system. The power allocation coefficient solution method based on a genetic algorithm solves problems that traditional gradient-based optimization methods cannot address, enabling the system to exhibit superior system outage probability under different antenna port numbers and antenna conditions.

Description

Technical Field

[0001] This invention relates to the field of wireless communication and power transmission technology, and in particular to a method and apparatus for resource allocation in a nonorthogonal multiple access system assisted by a fluid antenna. Background Technology

[0002] With the ever-increasing number of mobile user terminal connections, the design of next-generation mobile communication systems must consider the exponential increase in user density and traffic demand, posing unprecedented challenges to spectrum efficiency and system capacity. Non-Orthogonal Multiple Access (NOMA) technology has gradually gained widespread attention. This technology can address the challenges of massive connectivity by transmitting multiple user signals superimposed on the same time-frequency resource block at the transmitting end and decoding the multiplexed signals using serial interference cancellation (SIC) technology at the receiving end.

[0003] Most existing NOMA systems are based on a fixed-position antenna (FPA) architecture, meaning the base station's antenna array is fixed in position and cannot be adjusted. These systems struggle to adapt flexibly to changes in user location and channel conditions in complex or dynamic wireless environments, resulting in significant limitations in resource allocation. Specifically: 1. Fixed antenna structures cannot actively adjust antennas according to different users' channel conditions, limiting channel gain and making it difficult to extract the strongest signal using traditional selection diversity methods. 2. In multi-user scenarios, different users are at different distances from the base station, and different signals interfere with each other. Fixed antennas cannot reduce spatial interference through position adjustment, significantly reducing the outage probability for users at greater distances. 3. Some studies have not optimized the power allocation coefficient between two users, leading to significant differences in outage probabilities between users and thus reducing overall system performance.

[0004] To address these issues, novel antenna structures such as fluid antennas (FAs) have emerged in recent years. These antennas use software to control liquid structures or dielectric materials, allowing for real-time changes in position or shape to adapt to different channel conditions. Compared to traditional fixed antennas, FASs can dynamically adjust radiation characteristics, thereby optimizing signal transmission and significantly improving overall system performance and transmission reliability. However, current solutions generally lack model analysis of non-orthogonal multiple access systems assisted by fluid antennas, and the use of repeaters and optimized power distribution in two-user scenarios means that the overall system performance remains underdeveloped. Summary of the Invention

[0005] The purpose of this invention is to design a method and apparatus for resource allocation in a nonorthogonal multiple access system assisted by a flow antenna in order to solve the above-mentioned problems.

[0006] The present invention achieves the above objectives through the following technical solutions:

[0007] Resource allocation methods for nonorthogonal multiple access systems assisted by flow-mode antennas include:

[0008] S1. Construct a non-orthogonal multiple access system assisted by a fluid antenna. The non-orthogonal multiple access system includes a base station, a remote user, and a near-end user. The remote user is equipped with a single fixed antenna, and the near-end user is equipped with a single fixed antenna and a single one-dimensional fluid antenna.

[0009] S2. Determine the transmission channel model;

[0010] S3. Construct an objective function to minimize the maximum system interruption probability based on the constraints.

[0011] S4. Analyze the objective function based on the genetic algorithm to obtain the power allocation result that satisfies the constraints.

[0012] A resource allocation device for a fluid antenna-assisted nonorthogonal multiple access system includes:

[0013] Storage: Storage is used to store computer programs;

[0014] An actuator; the actuator is used to execute a computer program in the storage, which, when executed, implements the above-described method for allocating resources in a nonorthogonal multiple access system assisted by a flow antenna.

[0015] The beneficial effects of this invention are as follows: by introducing a fluid antenna and optimizing the non-orthogonal multiple access system, maximum ratio combining, and power allocation coefficients, the overall performance of the non-orthogonal multiple access system is significantly improved; the spatial freedom brought by the fluid antenna effectively enhances the channel quality, and the overall system outage probability is significantly lower than that of the traditional fixed antenna system, while satisfying the fairness between the two users in the system; the power allocation coefficient solution method based on genetic algorithm can solve the problems that traditional gradient-based optimization methods cannot solve, enabling the system to exhibit superior system outage probability under different antenna port numbers and antenna conditions. Attached Figure Description

[0016] Figure 1 This is a flowchart of the resource allocation method for a nonorthogonal multiple access system assisted by a flow-mode antenna according to the present invention;

[0017] Figure 2 A scenario model diagram of a direct transmission system for a nonorthogonal multiple access system assisted by a fluid antenna;

[0018] Figure 3 A scenario model diagram of a relay transmission system for a non-orthogonal multiple access system assisted by a fluid antenna. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0020] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0021] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0022] In the description of this invention, it should be understood that the terms "upper," "lower," "inner," "outer," "left," "right," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of this invention is in use, or the orientation or positional relationship commonly understood by those skilled in the art. They are only used to facilitate the description of this invention and to simplify the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0023] Furthermore, the terms "first," "second," etc., are used only to distinguish descriptions and should not be interpreted as indicating or implying relative importance.

[0024] In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, terms such as "set" and "connection" should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0025] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0026] like Figure 1 , Figure 2 , Figure 3 As shown, the resource allocation method for a nonorthogonal multiple access system assisted by a flow-mode antenna includes:

[0027] S1. Construct a non-orthogonal multiple access system assisted by a fluid antenna. The non-orthogonal multiple access system includes a base station, a remote user, and a near-end user. The remote user is equipped with a single fixed antenna, and the near-end user is equipped with a single fixed antenna and a single one-dimensional fluid antenna. The fluid antenna is pre-positioned to move within a one-dimensional linear area.

[0028] S2. Determine the transmission channel model; the channel model uses a block diagonal matrix for approximation. This modeling framework is inspired by the widely used block fading assumption in the time domain and supported by asymptotic results of the Toeplitz matrix. It has been proven to approximate the performance of a Slow-FAMA system well while maintaining resolvability. Under given conditions, the channel power gain of the desired signal received at the nth antenna port of the user equipment is characterized as a non-central chi-square random variable with two degrees of freedom. Then the probability density function of the channel power gain of the desired signal received by the user at the nth antenna port is... and cumulative distribution function Represented as: ; Where B represents the total number of spatial blocks, and These are the user index sets within the b-th block. The size of the b-th block; The correlation coefficient between antenna ports within the b-th block; It is an auxiliary random variable about the b-th block introduced during the derivation process; It is a zero-order modified Bessel function. It is a first-order Markum Q-function.

[0029] Meanwhile, regarding the relay scheme proposed in the system of this invention, such as Figure 3 As shown, near-end users As a relay node, it will decode the remote user Message sent to remote user To improve remote user experience The signal-to-noise ratio at the input is increased, thereby reducing the probability of interruption. The overall decoding implementation of the system is divided into two stages.

[0030] In the first phase, following the NOMA principle, the optimal decoding strategy is employed by near-end users equipped with flowing antennas. First, perform serial interference cancellation; this means that remote users equipped with flow antennas... The signal must be decoded directly while treating interference as noise. Simultaneously, it is assumed that only the optimal port maximizing the signal-to-interference-plus-noise ratio (SNR) received by the flowing antenna user is activated. After serial interference cancellation, the near-end user... Remove remote users from received signals. Information, decoding the required information. Meanwhile, remote users... Interference is treated as noise, the signal required for direct decoding. At this point, the near-end user in the superimposed transmitted information... The signal is considered interference.

[0031] remote users During the decoding process, a decode-forward decoding strategy was adopted, i.e., for near-end users. Due to the adoption of serial interference cancellation, remote user data has been acquired. Information, therefore, for near-end users After re-encoding, it is forwarded to . From transmitter and near-end user The received power is added together.

[0032] S3. Construct an objective function to minimize the maximum system outage probability based on constraints; specifically including:

[0033] S31, User i's first Signal received by each port Represented as: Where P is the transmission power; For path loss, For frequency-dependent loss, Let be the distance from the base station to user i. The path loss index; For user i's first Complex channel coefficients for each port; The power factor assigned to user i; The symbol sent to user i; For user i's first Additive white Gaussian noise at each port.

[0034] S32. According to the NOMA principle, serial interference cancellation is implemented by the near-end user u1. Assume that only the optimal port that maximizes the signal-to-interference-plus-noise ratio received by the user is activated, then the near-end user... Signal-to-interference-plus-noise ratio during serial interference cancellation Represented as: ,in, The optimal port for user i; This indicates that the mean is 0 and the variance is 0. Additive white Gaussian noise.

[0035] S33. After serial interference is eliminated, when decoding the required information, the near-end user... Remove remote users from received signals. Information, remote users If interference is treated as noise and the desired signal is directly decoded, then the near-end user... Signal-to-noise ratio Remote users Signal-to-interference-to-noise ratio Signal-to-interference-plus-noise ratio in relay process They are represented as follows: , , ;

[0036] S34, The outage probability of a non-orthogonal multiple access system includes near-end users. interruption probability and remote users interruption probability When the signal-to-interference-plus-noise ratio and near-end users are affected during the serial interference cancellation process. An interruption will not occur only when the received signal-to-noise ratio at each location is greater than its threshold. Considering that the interruption probability in NOMA scenarios is mainly affected by the power allocation factor β, therefore, for near-end users... interruption probability ,when hour, =1, otherwise, For direct transmission schemes, remote users Near-end users in the superimposed signal If it is directly considered as noise, then the remote user interruption probability ,when hour, =1, otherwise, For remote users in relay transmission solutions interruption probability Represented as:

[0037] ;

[0038] S35. The constraint is the power allocation coefficient for near-end users. Under the condition of satisfying the constraint, the objective function is constructed to minimize the maximum system outage probability P, expressed as: .

[0039] S4. Analyze the objective function using a genetic algorithm to obtain power allocation results that satisfy the constraints; specifically including:

[0040] S41. Treat the objective function as an individual, and calculate the population interruption probability performance of each individual as the fitness function. , is represented as: This step transforms the optimization problem of "minimizing the maximum interruption probability" into a search problem of "maximizing fitness".

[0041] S42. Using the roulette wheel selection method, analyze the probability of an individual being selected. , is represented as: ;

[0042] S43, based on probability Arithmetic crossover is performed on the selected individuals to generate new offspring. , is represented as: ;

[0043] S44. Apply a set mutation probability to the new offspring. Apply Gaussian mutation to obtain the mutated individuals;

[0044] S45. In each generation, retain the individual with the highest fitness.

[0045] S46. Determine if the termination condition has been met. If so, output the individual with the highest fitness among all generations as the optimal power allocation coefficient. Otherwise, return to S42. The termination condition is reached when either the first condition or the second condition is met; the first condition is the maximum number of iterations; the second condition is that the fitness function reaches a preset threshold.

[0046] A resource allocation device for a fluid antenna-assisted nonorthogonal multiple access system includes:

[0047] Storage: Storage is used to store computer programs;

[0048] An actuator; the actuator is used to execute a computer program in the storage, which, when executed, implements the above-described method for allocating resources in a nonorthogonal multiple access system assisted by a flow antenna.

[0049] The working principle of resource allocation in the non-orthogonal multiple access system assisted by the current-mode antenna of this invention is as follows:

[0050] This invention significantly improves the overall performance of non-orthogonal multiple access systems (NOAMS) by introducing a fluid antenna and optimizing the maximum ratio combining and power allocation coefficients. The spatial freedom provided by the fluid antenna effectively enhances channel quality, resulting in a significantly lower overall system outage probability compared to traditional fixed antenna systems, while simultaneously ensuring fairness between the two users. The power allocation coefficient solution based on a genetic algorithm addresses problems that traditional gradient-based optimization methods cannot solve, enabling the system to exhibit superior outage probability under varying antenna port numbers and conditions, thus validating the effectiveness and feasibility of this invention. This method allows the system to dynamically adjust power configuration according to demand, thereby significantly improving the overall system outage probability in two-user scenarios and meeting the power allocation requirements of NOAMS, achieving system user fairness.

[0051] The technical solutions of the present invention are not limited to the specific embodiments described above. Any technical modifications made in accordance with the technical solutions of the present invention fall within the protection scope of the present invention.

Claims

1. A resource allocation method for a nonorthogonal multiple access system assisted by a flow-mode antenna, characterized in that, include: S1. Construct a non-orthogonal multiple access system assisted by a fluid antenna. The non-orthogonal multiple access system includes a base station, a remote user, and a near-end user. The remote user is equipped with a single fixed antenna, and the near-end user is equipped with a single fixed antenna and a single one-dimensional fluid antenna. S2. Determine the transmission channel model; S3. Construct an objective function to minimize the maximum system interruption probability based on the constraints. S4. Analyze the objective function based on the genetic algorithm to obtain the power allocation result that satisfies the constraints.

2. The resource allocation method for a non-orthogonal multiple access system assisted by a flow-mode antenna according to claim 1, characterized in that, In S2, the channel model is approximated using a block diagonal matrix. Given conditions, the probability density function of the channel power gain of the desired signal received at the user's nth antenna port is... and cumulative distribution function , is represented as: ; Where B represents the total number of spatial blocks, and These are the user index sets within the b-th block. The size of the b-th block; The correlation coefficient between antenna ports within the b-th block; It is an auxiliary random variable about the b-th block introduced during the derivation process; It is a zero-order modified Bessel function. It is a first-order Markum Q-function.

3. The resource allocation method for a non-orthogonal multiple access system assisted by a flow-mode antenna according to claim 1, characterized in that, S3 includes: S31, User i's first Signal received by each port Represented as: Where P is the transmission power; For path loss, For frequency-dependent loss, Let be the distance from the base station to user i. The path loss index; For user i's first Complex channel coefficients for each port; The power factor assigned to user i; The symbol sent to user i; For user i's first Additive white Gaussian noise at each port; S32. Assuming that only the optimal port that maximizes the signal-to-interference-plus-noise ratio received by the user is activated, then the near-end user... Signal-to-interference-plus-noise ratio during serial interference cancellation Represented as: ,in, The optimal port for user i; This indicates that the mean is 0 and the variance is 0. Additive white Gaussian noise; S33. When decoding the required information, the near-end user Remove remote users from received signals Information, remote users If interference is treated as noise and the desired signal is directly decoded, then the near-end user... Signal-to-noise ratio Remote users Signal-to-interference-to-noise ratio Signal-to-interference-plus-noise ratio in relay process They are represented as follows: , , ; S34, The outage probability of a non-orthogonal multiple access system includes near-end users. interruption probability and remote users interruption probability For local users interruption probability ,when hour, =1, otherwise, For remote users using the direct transmission scheme interruption probability ,when hour, =1, otherwise, For remote users in relay transmission solutions interruption probability Represented as: ； S35. The constraint is the power allocation coefficient for near-end users. Under the condition of satisfying the constraint, the objective function is constructed to minimize the maximum system outage probability P, expressed as: .

4. The resource allocation method for a non-orthogonal multiple access system assisted by a flow-mode antenna according to claim 3, characterized in that, S4 includes: S41. Treat the objective function as an individual, and calculate the population interruption probability performance of each individual as the fitness function. , is represented as: ; S42. Using the roulette wheel selection method, analyze the probability of an individual being selected. , is represented as: ; S43, based on probability Arithmetic crossover is performed on the selected individuals to generate new offspring. , is represented as: ; S44. Apply a set mutation probability to the new offspring. Apply Gaussian mutation to obtain the mutated individuals; S45. In each generation, retain the individual with the highest fitness. S46. Determine if the termination condition has been met. If so, output the individual with the highest fitness among all generations as the optimal power allocation coefficient. Otherwise, return to S42.

5. The resource allocation method for a non-orthogonal multiple access system assisted by a flow-mode antenna according to claim 4, characterized in that, The termination condition is reached when either the first condition or the second condition is met; the first condition is the maximum number of iterations. The second condition is that the fitness function reaches a preset threshold.

6. A resource allocation device for a non-orthogonal multiple access system assisted by a flow antenna, characterized in that, include: Storage: Storage is used to store computer programs; Actuator; The actuator is used to execute a computer program in the storage, which, when executed, implements the resource allocation method for a nonorthogonal multiple access system assisted by a flow antenna as described in any one of claims 1-5.

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