A method and system for optimizing IRS-MU-OAM-OFDMA downlink resources based on QoS constraints

Abstract

The present invention discloses an IRS-MU-OAM-OFDMA downlink resource optimization method and system based on QoS constraints. The optimization method includes: establishing an IRS-MU-OAM-OFDMA communication system model; establishing a downlink resource optimization model of the IRS-MU-OAM-OFDMA communication system model based on QoS constraints; under the downlink resource optimization model, optimizing the sub-surface allocation factor, sub-carrier allocation factor, user power allocation coefficient, mode power allocation coefficient, and reflection phase respectively; and allocating communication resources according to the optimized sub-surface allocation factor, sub-carrier allocation factor, user power allocation coefficient, mode power allocation coefficient, and reflection phase. The disadvantage that the traditional OAM system can only be applied to line-of-sight channels is overcome. The system capacity is maximized under the premise of ensuring the QoS requirements of each user of the system, and the bit error rate is lower than that of the traditional method.

Description

Technical Field

[0001] The invention relates to an IRS-MU-OAM-OFDMA downlink resource optimization method and system based on QoS constraints, belonging to the technical field of communication. Background Art

[0002] Since the birth of wireless communications, dimensions such as time, frequency, and space have been widely utilized to maximize system capacity. However, existing resources are no longer sufficient to support the rapid growth of future communication needs, and there is an urgent need to explore new dimensions. Orbital Angular Momentum (OAM) has attracted widespread attention due to its potential unlimited capacity. Many scholars have studied combining OAM with multiple input multiple output (MIMO) and non-orthogonal multiple access (NOMA) technologies to improve system capacity.

[0003] However, traditional OAM systems are limited to Line-of-Sight (LOS) propagation environments. Although many studies have alleviated multiple interferences such as atmospheric turbulence, antenna misalignment, and multipath interference in single-user LOS environments, they still have high requirements for the propagation environment and cannot communicate normally in Non Line-of-Sight (NLOS) scenarios where the line-of-sight channel is blocked. Currently, there are few studies on NLOS-OAM systems. In addition, the research scenarios are all single-user, and the resource allocation scheme in multi-user (MU) NLOS-OAM systems has not been fully studied. Summary of the invention

[0004] The present invention provides an IRS-MU-OAM-OFDMA downlink resource optimization method and system based on QoS constraints. Based on Quality of Service (QoS) constraints and the generalized Snell reflection law, the Intelligent Reflective Surface (IRS) has become one of the potential technologies for 6G wireless communication due to its excellent effects in bypassing obstacles, enhancing network coverage, and suppressing interference. IRS has a large number of passive reflection units, which can actively change the amplitude and phase of the incident signal through software when the Channel State Information (CSI) is known, so as to achieve precise control and directional propagation of the vortex signal.

[0005] In order to realize multi-user NLOS OAM communication under blocking conditions, the present invention introduces an IRS that can be used as a relay to adjust the beam. The OAM system that introduces IRS can actively modify the wireless communication propagation environment, provide virtual line-of-sight links for dead zone users to enhance network coverage and system capacity, and overcome the limitations of large misalignment distortion of transmission and reception. At the same time, OFDMA technology is used. OFDMA (Orthogonal Frequency Division Multiple Access) is orthogonal frequency division multiple access, which is a multiple access technology based on the principle of orthogonal frequency division multiplexing (OFDM). OFDMA can not only increase the number of users and spectrum efficiency to enhance channel capacity, but also alleviate multipath interference. The problems disclosed in the background technology are solved.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] An IRS-MU-OAM-OFDMA downlink resource optimization method based on QoS constraints:

[0008] Establish the IRS-MU-OAM-OFDMA communication system model;

[0009] Establish a downlink resource optimization model for the IRS-MU-OAM-OFDMA communication system model based on QoS constraints;

[0010] Under the downlink resource optimization model, the sub-surface allocation factor, the sub-carrier allocation factor, the user power allocation coefficient, the mode power allocation coefficient, and the reflection phase are optimized respectively;

[0011] Communication resources are allocated according to the optimized sub-surface allocation factor, sub-carrier allocation factor, user power allocation coefficient, mode power allocation coefficient, and reflection phase.

[0012] Furthermore, the method of establishing the IRS-MU-OAM-OFDMA communication system model is as follows:

[0013] A transmitter module is provided, wherein the transmitter module is equipped with a uniform circular array to form a transmitting array; the transmitting array is composed of uniformly distributed antenna array elements, serving users; the antenna elements of the transmitting array are split into combinations, each combination acts as a transmitter for one user; the splitting method is the equal interval extraction method, so that each split combination is still a uniform circular array, and the adjacent antenna intervals and radii of each combination are completely consistent; the transmitting array is used to transmit the mode value for each user separately OAM vortex wave; the set of available modes is defined as ; The transmitter module adopts OFDMA technology, and the carrier set is , the number of subcarriers is ; A Cartesian coordinate system is established with the center of the transmitting array as the origin, the direction perpendicular to the horizon as the Y axis, and the direction parallel to the horizon as the Z axis; based on this coordinate system, The location of the root transmitting antenna is:

[0014] (1);

[0015] In the formula is the antenna azimuth, Represents the azimuth angle of the first reference antenna element specified in the uniform circular array in the reference coordinate system; is the radius of the transmitting array; is the pitch angle transformation in the rigid body transformation, is the roll angle transformation in the rigid body transformation, which is used to describe the rotation of the transmitting array around the X axis and rotate around the Y axis The radian makes the OAM beam point to the IRS to ensure that the signal transmission direction passes through the center of the sub-surface of the user; The location of the transmitting antenna of each user is described as the set ;in, The dimension is The set of real numbers has elements, each element is a three-dimensional vector, and the value of each element in the vector is a real number;

[0016] Set up the IRS module, the IRS module has a total of Reflection unit, The number of cells along the Z and Y directions, respectively. The sub-surface used by a single user is assigned units; a Cartesian coordinate system is established with the center of the transmitting array as the origin, the direction perpendicular to the horizon as the Y axis, and the direction parallel to the horizon as the Z axis; the position of the IRS is parallel to the YOZ plane, The coordinates of the center position of each sub-surface are described as: , No. The coordinates of the reflection unit of each sub-surface are expressed as: , the specific calculation is as follows:

[0017] (2);

[0018] in, , and Respectively represent the adjacent reflection units in and The interval of direction; Represents a set of center coordinates of a user-selectable subsurface:

[0019] (3);

[0020] A receiver module is provided, the receiver module comprising The user's receiver The users are randomly distributed in the area where the line of sight path to the transmitting array is blocked. Each user’s receiver is equipped with antennas The uniform circular array constitutes the receiving array; a Cartesian coordinate system is established with the center of the transmitting array as the origin, the direction perpendicular to the horizon as the Y axis, and the direction parallel to the horizon as the Z axis; based on this coordinate system, the The coordinates of the center position of the user receiving array are described as ;No. The receiving array The location of the root antenna is:

[0021] (4);

[0022] in, is the antenna azimuth, Represents the azimuth angle of the first reference antenna element specified in the uniform circular array in the reference coordinate system; represents the radius of the receiving array; is the pitch angle transformation in the rigid body transformation, is the roll angle transformation in the rigid body transformation, which is used to describe the rotation of the receiving array around the X axis and rotate around the Y axis The arc makes the OAM beam point to the IRS to ensure that the signal transmission direction passes through the center of the user's sub-surface; the user The locations of the receiving antennas are described as a set, ;

[0023] Rigid body transformation It is expressed as: (5);

[0024] Rigid body transformation It is expressed as: (6);

[0025] The channel for transmitting signals from the transmitter module to the IRS module and from the IRS module to the receiver module is the terahertz channel. Using Kirchhoff scattering theory and ray tracing technology, the equivalent terahertz channel is obtained:

[0026] (7);

[0027] In the formula Represents the free space loss in the terahertz frequency band and the distance between the transmitting and receiving antenna elements and wavelength Related; represents the molecular absorption loss, which is affected by the molecular temperature in the channel ,pressure and wavelength Influence; Represents the phase change affected by propagation distance in wireless communication;

[0028] The transmission channels between the transmitting array and IRS, and between the IRS and the user are line-of-sight channels; The transmitting antenna to the The reflection unit of the sub-surface The distance between the reflection units is:

[0029] (8);

[0030] No. The reflection unit of the sub-surface Reflection unit to User's The distance between the receiving antennas is: (9).

[0031] Furthermore, the method for establishing a downlink resource optimization model of an IRS-MU-OAM-OFDMA communication system based on QoS constraints is as follows:

[0032] Users of IRS-MU-OAM-OFDMA communication system The received signal is described as:

[0033] (10);

[0034] in, represents the channel matrix between the IRS and the receiver module based on equation (7). Similarly, represents the channel matrix between the transmit array and the IRS, where The dimension is The plural set of elements, each of which has elements are used to describe the phase of the reflector unit, and each phase value is a complex number; the transmitting array has Antennas, downlink channel matrix It is expressed as: ,in, The dimension is The plural set of elements, each of which has The elements are used to describe the phase of the reflection unit, and the value of each phase is a complex number; represents the IRS phase shift matrix in diagonal form; The mean is 0 and the variance is Additive Gaussian white noise; represents the modulated OAM signal, which is spiraled through the IDFT matrix accomplish: (11);

[0035] in, is the carrier allocation factor, describing the user Whether subcarriers are allocated , is the power of the carrier; Indicates that the user is assigned The power of each OAM mode; Indicates sending symbol; The dimension is The DFT matrix is (12);

[0036] The receiver module uses the DFT matrix With receiving signal Multiply to get the dehelical signal ; Based on the dehelical signal, information transmission is completed;

[0037] Based on Shannon's formula, users of IRS-MU-OAM-OFDMA communication system Rate (13);

[0038] in, Indicates the inter-mode interference caused by other modes of the user. represents Gaussian noise; Indicates user The pattern matrix is:

[0039] (14);

[0040] Considering the phase shift function of IRS, The continuous phase shift scenario is established to establish the optimization problem: (15);

[0041] Among them, the objective function is to maximize the system capacity; Indicates that one OFDM subcarrier can only be allocated to one user; Indicates that the sum of the transmission power of all users is not greater than the maximum feasible total power , and the transmission power of any user is positive; Indicates that the sum of the transmit powers of each OAM mode of each user is less than or equal to the transmit power of the user, and the transmit power of any mode of any user is non-negative; is the phase constraint of IRS; Indicates that the IRS used by the user can only be inner sub-surface; is a QoS constraint, so that the system capacity of any user is greater than .

[0042] Furthermore, under the downlink resource optimization model, the method for optimizing the sub-surface allocation factor, the sub-carrier allocation factor, the user power allocation coefficient, the mode power allocation coefficient, and the reflection phase is as follows:

[0043] The channel gain from the center of the transmitting antenna to the center of the sub-surface and then to the center of the receiving antenna is used. To describe the quality of the channel;

[0044] (16);

[0045] mark Used to describe the user. Used to describe sub-surfaces; traverse all and , we can get the channel gain set ; For each user Select Gain The maximum value of sub-surfaces; if multiple users have the best channel on the same sub-surface, the sub-surface is assigned to The user with the largest value is selected until there is no conflict, and the sub-surface allocation factor is determined;

[0046] On the basis of determining the sub-surface allocation factor, the reflection phase of the IRS is randomly generated multiple times, and after each random phase is generated, internal optimization is entered;

[0047] The internal optimization includes: keeping the phase unchanged and optimizing the subcarrier allocation factor; keeping the subcarrier allocation factor unchanged and optimizing the user power allocation coefficient; keeping the subcarrier allocation factor and the user power allocation coefficient unchanged and optimizing the mode power allocation coefficient of each user; keeping the subcarrier allocation factor, the user power allocation coefficient and the mode power allocation coefficient unchanged and optimizing the reflection phase;

[0048] Repeat the internal optimization until the convergence accuracy requirement is met or the number of iterations is reached;

[0049] Record each round of internal optimization solutions until the IRS-MU-OAM-OFDMA system capacity converges or the number of iterations is reached;

[0050] The output result is the suboptimal solution when the IRS-MU-OAM-OFDMA system capacity is maximized in the non-convex optimization resource allocation process, as well as the corresponding sub-surface allocation factor, subcarrier allocation factor, user power allocation coefficient, mode power allocation coefficient, and reflection phase.

[0051] Furthermore, the method of optimizing the subcarrier allocation factor while keeping the phase unchanged is as follows:

[0052] After the phase of the IRS module is randomly generated, the subcarrier allocation in the internal optimization is entered; the subcarriers allocated to each user are optimized based on QoS, and the user power is evenly distributed to all users, so that the transmission power of each user is ;user The transmission power of is evenly distributed to all modes, and the power of each mode is ; Based on randomly generated IRS phase Match each user as follows OFDM carrier with maximum capacity; if a carrier matches multiple users, the carrier is allocated to the user with the larger capacity value until there is no conflict; (17);

[0053] The above allocation uses a collection To describe, Indicates that it is distributed to users The number of available carriers is greater than or equal to the number of users, and the temporarily unassigned carrier set is Based on the formula The user with the worst QoS satisfaction: (18);

[0054] Secondary allocation is performed based on QoS satisfaction, that is, in the unallocated set The carriers that meet the formula are traversed and allocated to users , so that the user has dual carriers;

[0055] (19);

[0056] Update based on quadratic allocation , and the secondary allocation process is repeated continuously to obtain an OFDM subcarrier allocation scheme that meets the QoS requirements of all users; if there are still unallocated carriers at the end, all unallocated carriers are allocated to users with the best channel conditions to maximize system capacity, and the subcarrier allocation factor is obtained. .

[0057] Furthermore, keeping the subcarrier allocation factor unchanged, the method for optimizing the user power allocation coefficient is:

[0058] If a user occupies multiple subcarriers, the number of occupied subcarriers Determined during the carrier allocation process; assuming that any user The transmission power on its occupied subcarriers is equal, both ;Pick Mean of the diagonal elements As a user The channel gain, The mean As Gaussian noise; the water injection power allocation optimization model is established as follows:

[0059] (20);

[0060] Based on the Lagrange multiplier method, the optimal solution of the above equation can be obtained.

[0061] (twenty one);

[0062] Let the first-order derivative be 0, and we get:

[0063] (twenty two);

[0064] According to the formula The constraints of , we get the expression of water injection distribution:

[0065] (twenty three);

[0066] At this time, the transmission power of all users on their occupied carriers is obtained ,in ;user The total power is ; The set obtained at this time is It is the user power allocation coefficient.

[0067] Furthermore, the method for optimizing the mode power allocation coefficient of each user while keeping the subcarrier allocation factor and the user power allocation coefficient unchanged is:

[0068] After obtaining user power, optimize user The mode power on a single carrier sets up the following optimization problem: (twenty four);

[0069] right Performing a secondary transformation yields the following optimization:

[0070] (25);

[0071] Fixed power When The closed-form solution of is:

[0072] (26);

[0073] in, is the solution obtained in the previous iteration; fixed After that, the above optimization is a convex optimization problem. Based on the MATLAB CVX toolbox, we can solve any user Mode power ; Repeat the iteration to obtain a convergent solution. At this time, the set of solutions obtained is This is the mode power allocation coefficient; at this point, the user power and the corresponding mode power are allocated.

[0074] Furthermore, keeping the subcarrier allocation factor, user power allocation coefficient and mode power allocation coefficient unchanged, the method for optimizing the reflection phase is:

[0075] Based on the random IRS reflection phase, after the sub-surface allocation factor, sub-carrier allocation factor, user power allocation coefficient and mode power allocation coefficient are optimized, the IRS reflection phase is optimized alternately to obtain the following optimization model:

[0076] (27);

[0077] In order to solve the above non-convex problem, the mode channel gain is first written as: , ; then order , , ,but

[0078] (28);

[0079] in ,get

[0080] (29);

[0081] In the formula , ;

[0082] The optimization problem becomes: (30);

[0083] The constraints in the formula It is non-convex, and the semi-positive definite relaxation method is used for relaxation, and then the objective function is transformed into the following form through quadratic transformation:

[0084] (31);

[0085] in, (32);

[0086] is the known solution of the previous iteration; fixed After that, the optimization problem (31) becomes a convex optimization problem. The reflection phase is solved based on the MATLAB CVX toolbox and a converged solution is obtained after multiple iterations.

[0087] Accordingly, the present invention also provides an IRS-MU-OAM-OFDMA downlink resource optimization system based on QoS constraints, including an IRS-MU-OAM-OFDMA communication system and a controller;

[0088] The IRS-MU-OAM-OFDMA communication system includes a transmitter, an IRS and a receiver;

[0089] The transmitter is composed of a uniform circular array that is several times the number of antennas of a single user, and each uniform circular array is evenly divided into a plurality of sub-transmitter modules at equal intervals; each sub-transmitter module serves one user, is used to change the antenna direction based on channel state information, and is used to simultaneously transmit full-mode OAM signals to the smart reflector based on multiple OFDM subcarriers and beam steering technology;

[0090] The IRS is composed of a number of reflection units, which change the beam wavefront direction by changing the phase of the beam; based on the number of users, it is divided into multiple sub-surfaces, each sub-surface serves one user and is used to reflect the transmission signal that the user needs to receive to the receiver module of the user;

[0091] The receiver includes a plurality of user receiver modules composed of a uniform circular array, each user receiver module serves one user and is used to realize reception and demodulation of OAM signals;

[0092] The controller is provided with a downlink resource optimization module, which is used to optimize the sub-surface allocation factor, sub-carrier allocation factor, user power allocation coefficient, mode power allocation coefficient, and reflection phase to maximize the IRS-MU-OAM-OFDMA system capacity.

[0093] Further, the downlink resource optimization module includes an external optimization module and an internal optimization module;

[0094] The external optimization module is used to randomly generate the reflection phase of the IRS multiple times on the basis of determining the sub-surface allocation factor until the IRS-MU-OAM-OFDMA system capacity converges or the number of iterations is reached, so as to convert the unsolvable joint optimization into alternating optimization; after each random phase is generated, the internal optimization module is entered;

[0095] The internal optimization module is used to keep the phase unchanged and optimize the subcarrier allocation factor; keep the subcarrier allocation factor unchanged and optimize the user power allocation coefficient; keep the subcarrier allocation factor and the user power allocation coefficient unchanged and optimize the mode power allocation coefficient of each user; keep the subcarrier allocation factor, the user power allocation coefficient and the mode power allocation coefficient unchanged and optimize the reflection phase; until the convergence accuracy or the iteration number limit is met.

[0096] The beneficial effects achieved by the present invention are:

[0097] The present invention provides an IRS-MU-OAM-OFDMA downlink resource optimization method and system based on QoS constraints for multi-user OAM communication scenarios where there is no line-of-sight channel. The first advantage is that it overcomes the disadvantage that the traditional OAM system can only be applied to line-of-sight channels. The second advantage is that it maximizes the system capacity while ensuring the QoS requirements of each user of the system, and has a lower bit error rate than the traditional method. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] Figure 1 Schematic diagram of the IRS-MU-OAM-OFDMA system model.

[0099] Figure 2 Schematic diagram of the downlink resource optimization model optimization process. DETAILED DESCRIPTION

[0100] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0101] Embodiment 1:

[0102] This embodiment introduces an IRS-MU-OAM-OFDMA downlink resource optimization method based on QoS constraints, including the following steps:

[0103] Step 1: Establish the IRS-MU-OAM-OFDMA system model;

[0104] Step 2: Establish a downlink resource optimization model for the IRS-MU-OAM-OFDMA system model based on QoS constraints and define parameters;

[0105] Step 3: Under the downlink resource optimization model, the reflection surface position, carrier allocation, user power, mode power, and reflection phase are optimized respectively; based on the optimization results, a resource allocation plan is formed.

[0106] like Figure 1 As shown, the IRS-MU-OAM-OFDMA system model established in step 1 includes a transmitter module, an IRS module and a receiver module;

[0107] The transmitter module is equipped with a uniform circular array to form a transmitting array; the transmitting array is composed of uniformly distributed antenna array elements, serving users; the antenna elements of the transmitting array are split into combinations, each combination acts as a transmitter for one user; the splitting method is the equal interval extraction method, so that each split combination is still a uniform circular array, and the adjacent antenna intervals and radii of each combination are completely consistent; the transmitting array is used to transmit the mode value for each user separately OAM vortex wave; the set of available modes is defined as ; The transmitter module adopts OFDMA technology, and the carrier set is , the number of subcarriers is ; A Cartesian coordinate system is established with the center of the transmitting array as the origin, the direction perpendicular to the horizon as the Y axis, and the direction parallel to the horizon as the Z axis; based on this coordinate system, The location of the root transmitting antenna is:

[0108] (1);

[0109] In the formula is the antenna azimuth, Represents the azimuth angle of the first reference antenna element specified in the uniform circular array in the reference coordinate system; is the radius of the transmitting array; is the pitch angle transformation in the rigid body transformation, is the roll angle transformation in the rigid body transformation, which is used to describe the rotation of the transmitting array around the X axis and rotate around the Y axis The radian makes the OAM beam point to the IRS to ensure that the signal transmission direction passes through the center of the sub-surface of the user; The location of the transmitting antenna of each user is described as the set ;in, The dimension is The set of real numbers has elements, each element is a three-dimensional vector, and the value of each element in the vector is a real number;

[0110] Set up the IRS module, the IRS module has a total of Reflection unit, The number of cells along the Z and Y directions, respectively. The sub-surface used by a single user is assigned units; a Cartesian coordinate system is established with the center of the transmitting array as the origin, the direction perpendicular to the horizon as the Y axis, and the direction parallel to the horizon as the Z axis; the position of the IRS is parallel to the YOZ plane, The coordinates of the center position of each sub-surface are described as: , No. The coordinates of the reflection unit of each sub-surface are expressed as: , the specific calculation is as follows:

[0111] (2);

[0112] in, , and Respectively represent the adjacent reflection units in and The interval of direction; Represents a set of center coordinates of a user-selectable subsurface:

[0113] (3);

[0114] A receiver module is provided, the receiver module comprising The user's receiver The users are randomly distributed in the area where the line of sight path to the transmitting array is blocked. Each user’s receiver is equipped with antennas The uniform circular array constitutes the receiving array; a Cartesian coordinate system is established with the center of the transmitting array as the origin, the direction perpendicular to the horizon as the Y axis, and the direction parallel to the horizon as the Z axis; based on this coordinate system, the The coordinates of the center position of the user receiving array are described as ;No. The receiving array The location of the root antenna is:

[0115] (4);

[0116] in, is the antenna azimuth, Represents the azimuth angle of the first reference antenna element specified in the uniform circular array in the reference coordinate system; represents the radius of the receiving array; is the pitch angle transformation in the rigid body transformation, is the roll angle transformation in the rigid body transformation, which is used to describe the rotation of the receiving array around the X axis and rotate around the Y axis The arc makes the OAM beam point to the IRS to ensure that the signal transmission direction passes through the center of the user's sub-surface; the user The locations of the receiving antennas are described as a set, ;

[0117] Rigid body transformation It is expressed as:

[0118] (5);

[0119] Rigid body transformation It is expressed as:

[0120] (6);

[0121] The receiver module, IRS-MU-OAM-OFDMA communication system exists users, randomly distributed in a general urban area where the line of sight to the transmitting array is blocked, and each user has A uniform antenna array consisting of antennas.

[0122] Among them, the channel for transmitting signals from the transmitter module to the IRS module and from the IRS module to the receiver module is the terahertz channel. When terahertz waves propagate in free space, they will be affected by large free space attenuation and absorption by atmospheric molecules due to their high frequency and short wavelength. These factors make the path loss of the terahertz wave LoS link have significant frequency selectivity. In the THz MU-OAM system, the effects of free space attenuation and atmospheric molecule absorption need to be comprehensively considered to accurately calculate the path loss. Using Kirchhoff scattering theory and ray tracing technology, the equivalent terahertz LOS channel can be obtained:

[0123] (7);

[0124] The amplitude term in the formula represents the free space loss in the terahertz frequency band and the distance between the transmitting and receiving antenna elements. and wavelength The first exponential term represents the molecular absorption loss, which is affected by the molecular temperature in the channel ,pressure and wavelength The second exponential term represents the phase change affected by the propagation distance in wireless communication.

[0125] The transmission channels between the transmitting array and IRS, and between the IRS and the user are line-of-sight channels. The transmitting antenna to the The reflection unit of the sub-surface The distance between the reflection units is

[0126] (8);

[0127] Similarly, The reflection unit of the sub-surface Reflection unit to User's The distance between the receiving antennas is

[0128] (9);

[0129] For step 2, a downlink resource optimization model of the IRS-MU-OAM-OFDMA system model based on QoS constraints is established and parameters are defined. The steps are as follows:

[0130] The present invention assumes that the CSI of the channel is completely known. The received signal can be described as

[0131] (10);

[0132] In the formula represents the channel matrix between the IRS and the receiver based on equation (7). Similarly, represents the channel matrix between BS and IRS. Since BS has antennas, so the downlink channel matrix It can be expressed as . represents the IRS phase shift matrix in diagonal form. The mean is 0 and the variance is Additive white Gaussian noise. represents the modulated OAM signal, which is spiraled through the IDFT matrix accomplish:

[0133] (11);

[0134] In the formula is the carrier allocation factor, describing the user Whether subcarriers are allocated , is the power of the carrier. Indicates that the user is assigned The power of each OAM mode. Indicates the send symbol. The dimension is The DFT matrix is

[0135] (12);

[0136] The receiving end uses the DFT matrix With receiving signal Multiplying them together gives the de-helical signal Based on the de-helical signal, an equalization method such as MMSE is used to obtain an estimated signal, thus completing the end-to-end transmission of information.

[0137] Based on Shannon's formula, the number of users in the IRS-MU-OAM-OFDMA communication system is calculated. The achievable rate (13);

[0138] The first term in the denominator represents the inter-mode interference caused by other modes of the user, and the second term represents Gaussian noise. Indicates user The pattern matrix is: (14);

[0139] In order to simplify the problem, the present invention only considers the phase shift function of IRS, and is Therefore, the optimization problem is established:

[0140] (15);

[0141] The objective function is to maximize the system capacity. Indicates that one OFDM subcarrier can only be allocated to one user. Indicates that the sum of the transmission power of all users is not greater than the maximum feasible total power , and the transmission power of any user is positive. It means that the sum of the transmit powers of each OAM mode of each user is less than or equal to the transmit power of the user, and the transmit power of any mode of any user is non-negative. is the phase constraint of IRS. Indicates that the IRS used by the user can only be Inner sub-surface. is a QoS constraint, so that the system capacity of any user is greater than .

[0142] For step three, under the downlink resource optimization model, the sub-surface allocation factor, sub-carrier allocation coefficient, user power allocation coefficient, mode power allocation coefficient, and reflection phase are optimized respectively.

[0143] The optimization process is shown in Figure 2 and includes the following steps:

[0144] Step A: Determine the sub-surface allocation factor. Since there are many possible center coordinates of the sub-surface, the channel gain when pairing with each user is also diverse. Since the wavelength of each sub-carrier is similar to the terahertz channel characteristics, the channel gain from the center of the transmitting antenna to the center of the sub-surface and then to the center of the receiving antenna can be used. To describe the quality of the channel.

[0145] (16);

[0146] mark Used to describe the user. Used to describe sub-surfaces. Traverse all and , we can get the channel gain set Next, for each user Select Gain The maximum value of If multiple users have the best channel on the same sub-surface, the sub-surface is assigned to The user with the largest value is selected until there is no conflict.

[0147] Step B: Randomly generate phase. At this point, external optimization begins, IRS randomly generates phase, and enters internal optimization. Internal optimization includes 4 main steps: C1, C2, C3 and C4.

[0148] Step C1: Keep the phase unchanged and optimize the subcarrier allocation factor. After the phase of IRS is randomly generated, the subcarrier allocation in internal optimization is entered. This paper optimizes the subcarriers allocated to each user based on QoS, and the user power is evenly distributed to all users, so that the transmission power of each user is Similarly, users The transmission power of is evenly distributed to all modes, and the power of each mode is Next, an allocation is made based on the randomly generated IRS phase Match each user with the OFDM carrier with the maximum capacity as shown in equation (17). If there is a situation where one carrier matches multiple users, the carrier is allocated to the user with the larger capacity value until there is no conflict.

[0149] (17);

[0150] The above allocation can be done with the collection To describe, Indicates that it is distributed to users The number of available carriers is greater than or equal to the number of users, and the temporarily unassigned carrier set is Next, based on formula (18), we obtain the user with the worst QoS satisfaction.

[0151] (18);

[0152] Secondary allocation can be performed based on QoS satisfaction, that is, The carriers that meet the formula are traversed and allocated to users , so that the user has dual carriers.

[0153] (19);

[0154] Update based on quadratic allocation , and the secondary allocation process is repeated continuously to obtain an OFDM subcarrier allocation scheme that meets the QoS requirements of all users. Finally, there are still unallocated carriers, which can be allocated to users with the best channel conditions to maximize system capacity, that is, the subcarrier allocation factor is obtained. .

[0155] Step C2: Keep the subcarrier allocation factor unchanged and optimize the user power allocation coefficient. A user may occupy multiple subcarriers. It has been determined in the carrier allocation process. Assume that any user The transmission power on the subcarriers it occupies is equal, both In order to better use the water injection method for power distribution, Mean of the diagonal elements As a user The channel gain, The mean As Gaussian noise, the water injection power allocation optimization model is established as follows:

[0156] (20);

[0157] Based on the Lagrange multiplier method, the optimal solution of the above equation can be obtained.

[0158] (twenty one);

[0159] Let the first-order derivative be 0, we can get

[0160] (twenty two);

[0161] According to the constraints of formula (20), we can solve , and then the expression of water injection distribution is obtained

[0162] (twenty three);

[0163] At this time, the transmission power of all users on their occupied carriers is obtained ,in .user The total power is .

[0164] Step C3: Keep the subcarrier allocation factor and user power allocation coefficient unchanged, and optimize the mode power allocation coefficient of each user. After obtaining the user power, it is necessary to further allocate the mode power of each user. The mode power on a single carrier sets up the following optimization problem:

[0165] (twenty four);

[0166] The above problem has been simplified to the sum of multiple proportional functions that can be solved by Concave-Convex fractional programming method. Performing a secondary transformation can yield the following optimizations:

[0167] (25);

[0168] Fixed power When The closed-form solution of is:

[0169] (26);

[0170] In the formula is the solution obtained in the previous iteration. Fixed After that, the above optimization is a convex optimization problem, which can be solved based on the MATLAB CVX toolbox to find any user Mode power Repeat the iteration to get the converged solution. At this point, the user power and the corresponding mode power are allocated.

[0171] Step C4: Keep the subcarrier allocation factor, user and mode power allocation coefficients unchanged and optimize the reflection phase. Since the optimization process of the present invention is first based on the random IRS reflection phase, after the subsurface allocation factor, subcarrier allocation factor, user power allocation coefficient and mode power allocation coefficient are optimized, it is necessary to alternately optimize the IRS reflection phase to obtain the following optimization model:

[0172] (27);

[0173] In order to solve the above non-convex problem, the mode channel gain is first written as: , . Then , , ,but

[0174] (28);

[0175] in , and then get

[0176] (29);

[0177] In the formula , . Therefore, the optimization problem becomes:

[0178] (30);

[0179] The rank 1 constraint in the formula is non-convex and can be relaxed using the SDR method. The objective function is then transformed into the following form through a secondary transformation.

[0180] (31);

[0181] in (32);

[0182] is the known solution from the previous iteration. Fixed After that, the optimization problem (31) becomes a convex optimization problem, and the reflection phase can be solved based on the MATLAB CVX toolbox, and a converged solution can be obtained through multiple iterations.

[0183] Repeat steps C1, C2, C3, and C4 until the convergence accuracy requirement is met or the iteration limit is reached, and then exit the internal optimization. If the external optimization exit condition is not met, enter the external optimization again, otherwise the optimization will exit.

[0184] Step D: Take the suboptimal solution with the maximum numerical result. The output result is the suboptimal solution when the system capacity takes the maximum value in the non-convex optimization resource allocation process, as well as the corresponding sub-surface allocation factor, sub-carrier allocation factor, user power allocation coefficient, mode power allocation coefficient, and reflection phase.

[0185] Embodiment 2:

[0186] This embodiment provides an IRS-MU-OAM-OFDMA downlink resource optimization system based on QoS constraints, including an IRS-MU-OAM-OFDMA communication system composed of a transmitter module, an IRS module, and a receiver module, and a downlink resource optimization module for joint subcarriers, subreflection surfaces, user power, mode power, and reflection phase;

[0187] The transmitter is composed of a uniform circular array that is several times the number of antennas of a single user, and is evenly divided into multiple sub-transmitters at equal intervals. Each sub-transmitter serves one user, can change the antenna direction based on channel state information (CSI), and can simultaneously transmit full-mode OAM signals to the smart reflector based on multiple OFDM subcarriers and beam steering technology.

[0188] The IRS consists of several reflection units, which can change the beam wavefront direction by changing the phase of the beam. It can be divided into multiple sub-surfaces (sub-surfaces) based on the number of users. Each sub-surface serves one user and is responsible for reflecting the transmitted signal that the user needs to receive to the user's receiver.

[0189] The receiver comprises a plurality of user receivers formed by a uniform circular array, each user receiver serves one user and can realize reception and demodulation of OAM signals.

[0190] The downlink resource optimization module realizes the joint optimization of subcarriers, subreflection surfaces, user power, mode power and reflection phase, thereby maximizing the capacity of the IRS-MU-OAM-OFDMA communication system.

[0191] The downlink resource optimization includes: an external optimization module and an internal optimization module;

[0192] The external optimization module is used to randomly generate the reflection phase of the IRS multiple times based on the determination of the sub-surface allocation factor until the capacity of the IRS-MU-OAM-OFDMA communication system converges or the number of iterations is reached, so as to convert the unsolvable joint optimization into alternating optimization. After each random phase is generated, the internal optimization module is entered.

[0193] The internal optimization module includes the following four optimization steps. First, keep the reflection phase unchanged and optimize the subcarrier allocation factor. Second, keep the subcarrier allocation factor unchanged and optimize the user power allocation coefficient. Third, keep the reflection phase, subcarrier allocation factor, and user power allocation coefficient unchanged and optimize the mode power allocation coefficient within each user. Fourth, keep the subcarrier allocation factor, user power allocation coefficient, and mode power allocation coefficient unchanged and optimize the reflection phase until the convergence accuracy or iteration number limit is met.

[0194] The result output by the downlink resource optimization module is a suboptimal solution when the system capacity takes the maximum value in the non-convex optimization resource allocation process, as well as corresponding allocation parameters of reflection phase, carrier, power, and sub-reflection surface.

[0195] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

[0196] A computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, and when the instructions are executed by a computing device, the computing device performs an IRS-MU-OAM-OFDMA downlink resource optimization method based on QoS constraints.

[0197] A computing device includes one or more processors, one or more memories, and one or more programs, wherein the one or more programs are stored in the one or more memories and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing an IRS-MU-OAM-OFDMA downlink resource optimization method based on QoS constraints.

[0198] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0199] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0200] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0201] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0202] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A QoS-constrained IRS-MU-OAM-OFDMA downlink resource optimization method, characterized by: Establish the IRS-MU-OAM-OFDMA communication system model; Establish a downlink resource optimization model for the IRS-MU-OAM-OFDMA communication system model based on QoS constraints; Under the downlink resource optimization model, the sub-surface allocation factor, the sub-carrier allocation factor, the user power allocation coefficient, the mode power allocation coefficient, and the reflection phase are optimized respectively; Allocate communication resources according to the optimized sub-surface allocation factor, sub-carrier allocation factor, user power allocation coefficient, mode power allocation coefficient, and reflection phase; The method of establishing the IRS-MU-OAM-OFDMA communication system model is: A transmitter module is provided, wherein the transmitter module is equipped with a uniform circular array to form a transmitting array; the transmitting array is composed of uniformly distributed antenna array elements, serving users; the antenna elements of the transmitting array are split into combinations, each combination acts as a transmitter for one user; the splitting method is the equal interval extraction method, so that each split combination is still a uniform circular array, and the adjacent antenna intervals and radii of each combination are completely consistent; the transmitting array is used to transmit the mode value for each user separately OAM vortex wave; the set of available modes is defined as ; The transmitter module uses OFDMA technology, and the carrier set is , the number of subcarriers is ; Establish a Cartesian coordinate system with the center of the transmitting array as the origin, the direction perpendicular to the horizon as the Y axis, and the direction parallel to the horizon as the Z axis; Based on this coordinate system, the user No. The location of the root transmitting antenna is: （1）； In the formula is the antenna azimuth, Represents the azimuth angle of the first reference antenna element specified in the uniform circular array in the reference coordinate system; is the radius of the transmitting array; is the pitch angle transformation in the rigid body transformation, is the roll angle transformation in the rigid body transformation, which is used to describe the rotation of the transmitting array around the X axis and rotate around the Y axis The arc makes the OAM beam point to the IRS to ensure that the signal transmission direction passes through the center of the user's sub-surface; The locations of the transmitting antennas are described as the set ;in, The dimension is The set of real numbers has elements, each element is a three-dimensional vector, and the value of each element in the vector is a real number; Set up the IRS module, the IRS module has a total of Reflection unit, The number of cells along the Z and Y directions, respectively. The sub-surface used by a single user is assigned units; a Cartesian coordinate system is established with the center of the transmitting array as the origin, the direction perpendicular to the horizon as the Y axis, and the direction parallel to the horizon as the Z axis; the position of the IRS is parallel to the YOZ plane, The coordinates of the center position of each sub-surface are described as: , No. The coordinates of the reflection unit of each sub-surface are expressed as: , the specific calculation is as follows: （2）； in, , and Respectively represent the adjacent reflection units in and Direction interval; Represents a set of center coordinates of a user-selectable subsurface: （3）； A receiver module is provided, the receiver module comprising The user's receiver The users are randomly distributed in the area where the line of sight path to the transmitting array is blocked. Each user’s receiver is equipped with antennas The uniform circular array constitutes the receiving array; a Cartesian coordinate system is established with the center of the transmitting array as the origin, the direction perpendicular to the horizon as the Y axis, and the direction parallel to the horizon as the Z axis; based on this coordinate system, the user The coordinates of the center position of the receiving array are described as ;user The receiving array of The location of the root antenna is: （4）； in, is the antenna azimuth, Represents the azimuth angle of the first reference antenna element specified in the uniform circular array in the reference coordinate system; represents the radius of the receiving array; is the pitch angle transformation in the rigid body transformation, is the roll angle transformation in the rigid body transformation, which is used to describe the rotation of the receiving array around the X axis and rotate around the Y axis The arc makes the OAM beam point to the IRS to ensure that the signal transmission direction passes through the center of the user's sub-surface; the user The locations of the receiving antennas are described as a set, ; Rigid body transformation It is expressed as: (5); Rigid body transformation It is expressed as: (6); The channel for transmitting signals from the transmitter module to the IRS module and from the IRS module to the receiver module is the terahertz channel. Using Kirchhoff scattering theory and ray tracing technology, the equivalent terahertz channel is obtained: （7）； In the formula Represents the free space loss in the terahertz frequency band and the distance between the transmitting and receiving antenna elements and wavelength Related; represents the molecular absorption loss, which is affected by the molecular temperature in the channel ,pressure and wavelength Influence; Represents the phase change affected by propagation distance in wireless communication; The transmission channels between the transmitting array and IRS, and between the IRS and the user are line-of-sight channels; The transmitting antenna to the The subsurface The distance between the reflection units is: （8）； No. The reflection unit of the sub-surface Reflector unit to user No. The distance between the receiving antennas is: (9); The method for establishing a downlink resource optimization model for an IRS-MU-OAM-OFDMA communication system based on QoS constraints is as follows: Users of IRS-MU-OAM-OFDMA communication system The received signal is described as: （10）； in, represents the channel matrix between the IRS and the receiver module based on equation (7). Similarly, represents the channel matrix between the transmit array and the IRS, where The dimension is The plural set of elements, each of which has elements are used to describe the phase of the reflector unit, and each phase value is a complex number; the transmitting array has Antenna, downlink channel matrix It is expressed as: ,in, The dimension is The plural set of elements, each of which has The elements are used to describe the phase of the reflection unit, and the value of each phase is a complex number; represents the IRS phase shift matrix in diagonal form; The mean is 0 and the variance is Additive Gaussian white noise; represents the modulated OAM signal, which is spiraled through the IDFT matrix accomplish: (11); in, is the carrier allocation factor, describing the user Whether subcarriers are allocated , is the power of the carrier; Indicates that the user is assigned The power of each OAM mode; Indicates sending symbol; The dimension is The DFT matrix is （12）； The receiver module uses the DFT matrix With receiving signal Multiply to get the dehelical signal ; Based on the dehelical signal, information transmission is completed; Based on Shannon's formula, users of IRS-MU-OAM-OFDMA communication system Rate （13）； in, Indicates the inter-mode interference caused by other modes of the user. represents Gaussian noise; Indicates user The pattern matrix is: （14）； Considering the phase shift function of IRS, The continuous phase shift scenario is established to establish the optimization problem: （15）； Among them, the objective function is to maximize the system capacity; Indicates that one OFDM subcarrier can only be allocated to one user; Indicates that the sum of the transmission power of all users is not greater than the maximum feasible total power , and the transmission power of any user is positive; Indicates that the sum of the transmit powers of each OAM mode of each user is less than or equal to the transmit power of the user, and the transmit power of any mode of any user is non-negative; is the phase constraint of IRS; Indicates that the IRS used by the user can only be inner sub-surface; is a QoS constraint, so that the system capacity of any user is greater than ; Under the downlink resource optimization model, the method for optimizing the sub-surface allocation factor, the sub-carrier allocation factor, the user power allocation coefficient, the mode power allocation coefficient, and the reflection phase is as follows: The channel gain from the center of the transmitting antenna to the center of the sub-surface and then to the center of the receiving antenna is used. To describe the quality of the channel; （16）； mark Used to describe the user. Used to describe sub-surfaces; traverse all and , we can get the channel gain set ; For each user Select Gain The maximum value of sub-surfaces; if multiple users have the best channel on the same sub-surface, the sub-surface is assigned to The user with the largest value is selected until there is no conflict, and the sub-surface allocation factor is determined; On the basis of determining the sub-surface allocation factor, the reflection phase of the IRS is randomly generated multiple times, and after each random phase is generated, internal optimization is entered; The internal optimization includes: keeping the phase unchanged and optimizing the subcarrier allocation factor; keeping the subcarrier allocation factor unchanged and optimizing the user power allocation coefficient; keeping the subcarrier allocation factor and the user power allocation coefficient unchanged and optimizing the mode power allocation coefficient of each user; keeping the subcarrier allocation factor, the user power allocation coefficient and the mode power allocation coefficient unchanged and optimizing the reflection phase; Repeat the internal optimization until the convergence accuracy requirement is met or the number of iterations is reached; Record each round of internal optimization solutions until the IRS-MU-OAM-OFDMA system capacity converges or the number of iterations is reached; The output result is the suboptimal solution when the IRS-MU-OAM-OFDMA system capacity is maximized in the non-convex optimization resource allocation process, as well as the corresponding sub-surface allocation factor, subcarrier allocation factor, user power allocation coefficient, mode power allocation coefficient, and reflection phase.

2. The IRS-MU-OAM-OFDMA downlink resource optimization method based on QoS constraints according to claim 1, characterized in that: Keeping the phase unchanged, the method to optimize the subcarrier allocation factor is: After the phase of the IRS module is randomly generated, the subcarrier allocation in the internal optimization is entered; the subcarriers allocated to each user are optimized based on QoS, and the user power is evenly distributed to all users, so that the transmission power of each user is ;user The transmission power of is evenly distributed to all modes, and the power of each mode is ; Based on randomly generated IRS phase Match each user with an OFDM carrier with the maximum capacity as shown in equation (17); if there is a situation where one carrier matches multiple users, the carrier is allocated to the user with the larger capacity value until no conflict occurs; （17）； The above allocation uses a collection To describe, Indicates that it is distributed to users The number of available carriers is greater than or equal to the number of users, and the temporarily unassigned carrier set is ; Based on formula (18), the user with the worst QoS satisfaction is obtained: (18); Secondary allocation is performed based on QoS satisfaction, that is, in the unallocated set The carriers that satisfy equation (19) are traversed and allocated to users. , so that the user has dual carriers; （19）； Update based on quadratic allocation , and the secondary allocation process is repeated continuously to obtain an OFDM subcarrier allocation scheme that meets the QoS requirements of all users; if there are still unallocated carriers at the end, all unallocated carriers are allocated to users with the best channel conditions to maximize system capacity, and the subcarrier allocation factor is obtained. .

3. The IRS-MU-OAM-OFDMA downlink resource optimization method based on QoS constraints according to claim 2, characterized in that: Keeping the subcarrier allocation factor unchanged, the method to optimize the user power allocation coefficient is: If a user occupies multiple subcarriers, the number of occupied subcarriers Determined during the carrier allocation process; assuming that any user The transmission power on its occupied subcarriers is equal, both ;Pick Mean of the diagonal elements As a user The channel gain, The mean As Gaussian noise; the water injection power allocation optimization model is established as follows: （20）； Based on the Lagrange multiplier method, the optimal solution of the above equation can be obtained. （21）； Let the first-order derivative be 0, and we get: （22）； According to the constraints of formula (20), we can solve , we get the expression of water injection distribution: （23）； At this time, the transmission power of all users on their occupied carriers is obtained ,in ;user The total power is ; The set obtained at this time is It is the user power allocation coefficient.

4. The IRS-MU-OAM-OFDMA downlink resource optimization method based on QoS constraints according to claim 3, characterized in that: The method of optimizing the mode power allocation coefficient of each user while keeping the subcarrier allocation factor and the user power allocation coefficient unchanged is: After obtaining user power, optimize user The mode power on a single carrier sets up the following optimization problem: （24）； right Performing a secondary transformation yields the following optimization: （25）； Fixed power When The closed-form solution of is: （26）； in, is the solution obtained in the previous iteration; fixed After that, the above optimization is a convex optimization problem. Based on the MATLAB CVX toolbox, it can be solved for any user Mode power ; Repeat the iteration to obtain a convergent solution. At this time, the set of solutions obtained is This is the mode power allocation coefficient; at this point, the user power and the corresponding mode power are allocated.

5. The IRS-MU-OAM-OFDMA downlink resource optimization method based on QoS constraints according to claim 4, characterized in that: Keeping the subcarrier allocation factor, user power allocation coefficient and mode power allocation coefficient unchanged, the method for optimizing the reflection phase is: Based on the random IRS reflection phase, after the sub-surface allocation factor, sub-carrier allocation factor, user power allocation coefficient and mode power allocation coefficient are optimized, the IRS reflection phase is optimized alternately to obtain the following optimization model: （27）； In order to solve the non-convex problem shown in equation (27), the mode channel gain is first written as: , ; Re-order , , ,but （28）； in ,get （29）； In the formula , ; The optimization problem becomes: （30）； The constraints in the formula It is non-convex, and the semi-positive definite relaxation method is used for relaxation, and then the objective function is transformed into the following form through quadratic transformation: （31）； in, (32); is the known solution of the previous iteration; fixed After that, the optimization problem (31) becomes a convex optimization problem. The reflection phase is solved based on the MATLAB CVX toolbox, and a converged solution is obtained after multiple iterations.

6. An IRS-MU-OAM-OFDMA downlink resource optimization system based on QoS constraints, characterized by: Including IRS-MU-OAM-OFDMA communication system and controller; The IRS-MU-OAM-OFDMA communication system includes a transmitter, an IRS and a receiver; The transmitter is composed of a uniform circular array that is several times the number of antennas of a single user, and each uniform circular array is evenly divided into a plurality of sub-transmitter modules at equal intervals; each sub-transmitter module serves one user, is used to change the antenna direction based on channel state information, and is used to simultaneously transmit full-mode OAM signals to the smart reflector based on multiple OFDM subcarriers and beam steering technology; The IRS is composed of a number of reflection units, which change the beam wavefront direction by changing the phase of the beam; based on the number of users, it is divided into multiple sub-surfaces, each sub-surface serves one user and is used to reflect the transmission signal that the user needs to receive to the receiver module of the user; The receiver includes a plurality of user receiver modules composed of a uniform circular array, each user receiver module serves one user and is used to realize reception and demodulation of OAM signals; The controller is provided with a downlink resource optimization module, which is used to optimize the sub-surface allocation factor, the sub-carrier allocation factor, the user power allocation coefficient, the mode power allocation coefficient, and the reflection phase so that the capacity of the IRS-MU-OAM-OFDMA system reaches the maximum value; The downlink resource optimization module includes an external optimization module and an internal optimization module; The external optimization module is used to randomly generate the reflection phase of the IRS multiple times on the basis of determining the sub-surface allocation factor until the IRS-MU-OAM-OFDMA system capacity converges or the number of iterations is reached, so as to convert the unsolvable joint optimization into alternating optimization; after each random phase is generated, the internal optimization module is entered; The internal optimization module is used to keep the phase unchanged and optimize the subcarrier allocation factor; keep the subcarrier allocation factor unchanged and optimize the user power allocation coefficient; keep the subcarrier allocation factor and the user power allocation coefficient unchanged and optimize the mode power allocation coefficient of each user; keep the subcarrier allocation factor, the user power allocation coefficient and the mode power allocation coefficient unchanged and optimize the reflection phase; until the convergence accuracy or the iteration number limit is met.

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