A method and system for resource allocation of Massive MIMO with a single-bit smart reflector.

Abstract

The application provides a kind of single-bit intelligent reflecting surface Massive MIMO resource allocation method and system, belong to wireless communication field.In order to solve the problem that the phase shift of intelligent reflecting surface is not considered as a discrete variable in the wireless communication system based on intelligent reflecting surface, it is difficult to obtain the optimal continuous transmission power and the optimal discrete phase shift effect when considering multiple factors.The application considers the requirements of resource, response time, system stability and dynamic expansion capability, and obtains an economic and efficient resource allocation method by limiting the transmission power and channel capacity of the transmission antenna, sets a penalty function using range limitation, fully considers the importance of channel capacity and transmission power by normalization and weight to formulate the objective function, and provides a new idea for realizing green and efficient Massive MIMO wireless communication.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and more specifically, to a method and system for allocating Massive MIMO resources using a single-bit intelligent reflector. Background Technology

[0002] With the rapid development of wireless communication technology, people have increasingly higher requirements for the performance of communication systems, such as higher data transmission rates, wider coverage, and lower energy consumption. Therefore, intelligent reflective surfaces have emerged as an emerging wireless communication technology. By integrating a large number of low-cost passive or active reflective elements on a plane, they can solve many challenges faced by traditional wireless communication systems, such as signal fading, interference, and limited spectrum resources.

[0003] Meanwhile, with the rapid development of mobile internet, the Internet of Things, and multimedia communications, users' demands for high data rates and low latency are constantly increasing, making improving channel capacity a key aspect of communication technology development. Channel capacity represents the maximum capacity a channel can support under given channel conditions and can be calculated using Shannon's formula. This provides a theoretical upper limit for the design of communication systems, determining the system's performance under optimal conditions. Therefore, maximizing channel capacity at low cost is a crucial direction for breaking through 5G and moving towards 6G.

[0004] However, due to limitations in resources, response time, system stability, and dynamic scalability, it's impossible to maintain maximum channel capacity indefinitely, as this contradicts resource utilization principles. Therefore, channel capacity should be limited to a certain range. Furthermore, considering economic factors, the transmit power of base station antennas should also be limited to a certain range. Thus, a suitable mechanism should be found to achieve an ideal balance.

[0005] Massive MIMO wireless communication based on intelligent reflectors: Ahmad Sirojuddin et al.'s paper, "Low-Complexity Sum-Capacity Maximization for Intelligent Reflecting Surface-Aided MIMO Systems," describes a low-complexity algorithm to maximize system capacity for intelligent reflectors with continuous phase shifts, but it does not consider the case where the phase shift of the intelligent reflector is a discrete variable. Sun Kexin et al.'s proposal, "Resource Allocation Method and System for Intelligent Reflecting Surface-Aided Massive MIMO," improves system capacity by jointly optimizing the continuous transmit power variable of the transmit antenna and the continuous phase shift variable of the intelligent reflector, but similarly does not consider the case where the phase shift of the intelligent reflector is a discrete variable.

[0006] Literature review revealed that most studies on Massive MIMO wireless communication systems based on smart reflectors focus on the continuous transmit power variables of the transmit antenna and the continuous phase shift variables of the smart reflector. Furthermore, most studies use maximizing channel capacity and a penalty function as optimization objectives. No literature has been found that utilizes range constraints to set a penalty function based on the continuous transmit power variables of the transmit antenna and the discrete phase shift variables of the smart reflector, and formulates the objective function through normalization and weighting. Summary of the Invention

[0007] The technical problem to be solved by this invention is:

[0008] To address the issue that wireless communication systems based on smart reflectors fail to consider the phase shift of the smart reflector as a discrete variable, which makes it difficult to obtain the optimal continuous transmit power and optimal discrete phase shift effect when considering multiple factors.

[0009] The technical solution adopted by the present invention to solve the above-mentioned technical problems is as follows:

[0010] This invention provides a method for allocating resources for Massive MIMO with a single-bit smart reflector, comprising the following steps:

[0011] S100. Establish a system model including base stations, smart reflectors, and users. By jointly optimizing the transmit power of the transmit antenna and the phase angle of the reflector element in the smart reflector, range constraints are imposed on the transmit power of the transmit antenna and the channel capacity. The objective function is formulated through normalization and weighted processing.

[0012] S200: Set the parameters of the quantum black-winged kite mechanism and initialize the quantum position of each quantum black-winged kite. Measure the initial quantum position of each quantum black-winged kite through the measurement equation to obtain the initial measurement position of each quantum black-winged kite. Map the initial measurement position of each quantum black-winged kite to a resource allocation method according to the mapping rule and substitute it into the system model of step S100. Set a fitness function with penalty according to the constraint conditions and calculate the fitness values ​​of all initial positions to determine the local optimal quantum position, the local optimal measurement position, the global optimal quantum position, and the global optimal measurement position.

[0013] S300. Each quantum black-winged kite updates its quantum position and measurement position according to the attack behavior. According to the mapping rule, each quantum black-winged kite after the attack behavior is mapped to a resource allocation method and substituted into the system model of step S100. The corresponding fitness value is calculated, and the local optimal quantum position, local optimal measurement position, global optimal quantum position and global optimal measurement position are updated again.

[0014] S400. After each quantum black-winged kite performs the attack behavior in step S300, its quantum position and measurement position are updated according to the migration behavior. According to the mapping rule, each quantum black-winged kite after performing the migration behavior is mapped to a resource allocation method and substituted into the system model in step S100. The corresponding fitness value is calculated, and the local optimal quantum position, local optimal measurement position, global optimal quantum position, and global optimal measurement position are updated again.

[0015] S500: Determine whether the search mechanism has reached the maximum number of iterations. If yes, output the globally optimal measurement position; otherwise, return to step S300 and continue iterating until the maximum number of iterations is reached and the globally optimal measurement position is output.

[0016] S600: Map the globally optimal measurement position obtained in step S500 according to the mapping rules in step S200 to obtain an economical and efficient resource allocation method.

[0017] Further, in step S100, the following is included:

[0018] Assume base stations have There are one antenna, and the user has... One antenna, both smart reflectors have There are one reflection unit, and the reflection matrices of the two intelligent reflection surfaces are respectively... and ,in This represents a diagonal matrix formed by the elements of the set in sequence. For the first The first intelligent reflective surface The coefficient of each reflecting unit, For the first The first intelligent reflective surface The phase of each reflecting unit, , The imaginary unit, For the first The first intelligent reflective surface Phase angle of each reflecting unit, , For the two discrete phase angles required, , , and These are the sets of phase angles for the first and second intelligent reflective surfaces, respectively. , ;

[0019] Let the base station transmit a unit matrix. The transmit power matrix of the transmitting antenna is denoted as ,in For the first The transmit power of the root transmitting antenna, , and These represent the maximum and minimum allowable transmit power for each transmitting antenna, respectively. The user received signal matrix is ​​represented as follows: The channel state information from the base station to the first smart reflector is represented as follows: The channel state information from the first intelligent reflector to the user is represented as: The channel state information from the first smart reflector to the second smart reflector is represented as follows: The channel state information from the second intelligent reflector to the user is represented as follows: ;

[0020] Assume that the noise received at both smart reflectors and the user terminal is complex Gaussian white noise, expressed as follows: , and Therefore, the user's received signal can be represented as ,in The signal transmitted by the base station received by the user. The noise signal received at the first smart reflector is the noise signal transmitted to the user. The noise signal from the received noise at the second smart reflector to the user; the channel capacity is expressed as... ,in This indicates finding the value of the determinant of a matrix. Represented as An identity matrix of dimension 1 The signal power matrix from the base station to the user after passing through the first smart reflector. This indicates finding the conjugate transpose of a matrix. The signal power matrix for the base station transmitting signals through the first and second smart reflectors to the user. For the user's noise power matrix, The noise power matrix from the received noise at the first smart reflector to the user is given. The noise power matrix from the received noise at the first smart reflector to the user via the first and second smart reflectors is given. The noise power matrix from the received noise at the second smart reflector to the user is given. , and These are the noise power figures at the first smart reflector, the second smart reflector, and the user's location, respectively.

[0021] Let the objective function be ,in , , The summation symbol is used to represent the summation symbol. The symbol represents the absolute value. , and For user-expected parameters, and These represent the maximum and minimum values ​​of the required channel capacity.

[0022] Further, in step S200, the following are included:

[0023] Suppose there exists Quantum Black-winged Kite, each Quantum Black-winged Kite has dimension, ;No. The generation The quantum position of the quantum black-winged kite is ,in , For the number of iterations, The maximum number of iterations, , First generation of seasons , The quantum position of the quantum black-winged kite All dimensions are random numbers uniformly distributed between [0,1]. Dimensions set as Then the first The set of quantum positions of all quantum black-winged kites is ;

[0024] The first was obtained through measurement. The generation The measurement position of the quantum black-winged kite is , No. The generation Quantum Black-winged Kite The equation for measuring a quantum position is: ,in The random numbers are uniformly distributed between [0,1]. Mapped to resource allocation method according to mapping rules. , , And substitute it into the system model, where the mapping rule is: , The first The generation The transmission power of the antenna chosen by the quantum black-winged kite and the phase angle of the smart reflector, Represented as the first The generation The quantum black-winged kite's chosen first The transmit power of the root transmitting antenna, Represented as the first The generation The quantum black-winged kite's chosen first One phase angle, The two fixed discrete phase angles are provided for selection;

[0025] The penalty function is set according to the constraints. ,in The penalty coefficient is used to calculate the first... The generation The quantum black-winged kite has a penalty fitness value. ; will the first Quantum Black-winged Kite as of the [number]th The quantum position with the smallest fitness value up to date and the measurement position are denoted as the nth quantum position. Local optimal quantum position of the quantum black-winged kite and local optimal measurement location All quantum black-winged kites up to the [number]th The quantum position with the minimum fitness value and the measured position are denoted as the global optimal quantum position. and the global optimal measurement location .

[0026] Further, in step S300, the following are included:

[0027] Set a selection probability Then, random numbers uniformly distributed between [0,1] are generated. ,in ;

[0028] like Define the first attack action after execution. The generation Quantum Black-winged Kite The quantum rotation angle is ,in , , , , and All are random numbers that follow a uniform distribution between [0,1]. and For the first Quantum Black-winged Kite The local optimal quantum position and the local optimal measurement position of dimensionality. and For the first Global optimal quantum position and global optimal measurement position. , , , and All are constant parameters;

[0029] like Define the first attack action after execution. The generation Quantum Black-winged Kite The quantum rotation angle is ,in , ;

[0030] After defining the execution of the attack behavior, the first The generation Quantum Black-winged Kite Quantum position The evolutionary method is as follows:

[0031] ,in A random number that follows a uniform distribution between [0,1]. The value represents the probability that a qubit will mutate when the quantum rotation angle is 0. The constants between;

[0032] After obtaining the execution of the attack, the first The generation The quantum position of the quantum black-winged kite is Then update the number of attacks executed. The set of quantum positions of the quantum black-winged kite is ; The measurement of the number of seconds after the attack was executed The generation The measurement position of the quantum black-winged kite is After executing the attack, the first The generation Quantum Black-winged Kite The equation for measuring a quantum position is: ,in A random number that follows a uniform distribution between [0,1].

[0033] Will Mapped to resource allocation method according to mapping rules. Substituting this into the wireless communication system model, the calculation is performed after the attack is executed. The generation The quantum black-winged kite has a fitness value with a penalty. The mapping rule is as follows: , Each is the 1st time after the attack is executed The generation The transmission power of the antenna chosen by the quantum black-winged kite and the phase angle of the smart reflector, This indicates the number of times the attack has been performed. The generation The quantum black-winged kite's chosen first The transmit power of the root transmitting antenna, This indicates the number of times the attack has been performed. The generation The quantum black-winged kite's chosen first The phase angle; the first phase angle; Local optimal quantum position of the quantum black-winged kite and local optimal measurement location Updated to execute attack behavior up to the [number]th Replace the quantum position with the minimum fitness value and the measurement position with the globally optimal quantum position. and the global optimal measurement location Updated to include all Quantum Blackwing Kite units up to the date of the attack. The quantum position and measurement position with the smallest fitness value up to date.

[0034] Further, in step S400, the following is included:

[0035] like Define the number of migration actions performed after the migration is executed. The generation Quantum Black-winged Kite The quantum rotation angle is ,in This indicates the number of times the attack was performed. Randomly select the number from other quantum black-winged kites. The measurement location of the quantum black-winged kite. , , and All are random numbers that follow a uniform distribution between [0,1]. and To be the first after the attack is executed The generation Quantum Black-winged Kite The quantum position and measurement position of a dimension. and To be the first after the attack is executed The generation Quantum Black-winged Kite The local optimal quantum position and the local optimal measurement position of dimensionality. and To be the first after the attack is executed The generation Global optimal quantum position and global optimal measurement position. These are constant parameters;

[0036] like Define the number of migration actions performed after the migration is executed. The generation Quantum Black-winged Kite The quantum rotation angle is ,in and To be the first after the attack is executed Randomly select the number from other quantum black-winged kites. and No. The quantum position of the black-winged kite in dimensionality. and To be the first after the attack is executed Randomly select the number from other quantum black-winged kites. and No. The quantum black-winged kite's measurement position. A random number that follows a uniform distribution between [0,1]. Randomly select from 0 and 1;

[0037] Define the first step after executing the migration behavior The generation Quantum Black-winged Kite The evolution method of quantum position is as follows ,in A random number that follows a uniform distribution between [0,1]. The value represents the probability that a qubit will mutate when the quantum rotation angle is 0. The constants between;

[0038] After obtaining the execution of the migration action, the first The generation The quantum position of the black-winged kite is Then update the migration behavior after it is executed. The quantum position set of the black-winged kite is ; The measurement of the number of days after the migration was carried out The generation The quantum black-winged kite measured the position as follows: After the migration was carried out The generation Quantum Black-winged Kite The equation for measuring a quantum position is: ,in A random number that follows a uniform distribution between [0,1].

[0039] Will Mapping to a resource allocation method according to the mapping rules in step S200. Substitute it into the wireless communication system model to calculate the number of steps after the migration behavior is executed. The generation The quantum black-winged kite has a fitness value with a penalty. ; will the first Local optimal quantum position of the quantum black-winged kite and local optimal measurement location Updated again to indicate that after the execution of the migration, up to the [number]th [year] Replace the quantum position with the minimum fitness value and the measurement position with the globally optimal quantum position. and the global optimal measurement location Updated again to include all quantum black-winged kites after the migration process, up to the [number missing]. The quantum position and measurement position with the smallest fitness value up to date.

[0040] A single-bit intelligent reflector Massive MIMO resource allocation system is provided. The system has a program module corresponding to the steps described above, and executes the steps in the single-bit intelligent reflector Massive MIMO resource allocation method described above when running.

[0041] A computer-readable storage medium storing a computer program configured to, when invoked by a processor, implement the steps of a Massive MIMO resource allocation method for a single-bit smart reflector.

[0042] Compared with the prior art, the beneficial effects of the present invention are:

[0043] (1) Considering the requirements of resources, response time, system stability and dynamic expansion capability, this invention obtains an economical and efficient resource allocation method by limiting the transmit power of the transmit antenna and the channel capacity. It sets a penalty function by using range limitation and formulates the objective function by fully considering the importance of both channel capacity and transmit power through normalization and weighting. Since the objective function in most literature is to maximize the channel capacity, this invention provides a new idea for realizing green and efficient Massive MIMO wireless communication.

[0044] (2) This invention designs a quantum black-winged kite mechanism for high-dimensional hybrid optimization problems, combining quantum rotation angle and quantum rotation gate with the black-winged kite mechanism, overcoming its application limitations in solving discrete optimization problems and hybrid optimization problems while still maintaining superior performance. In addition, compared with alternating optimization, this invention can simultaneously optimize both continuous emission power and discrete phase shift of the intelligent reflector.

[0045] (3) Simulation results show that the present invention exhibits good robustness under different weighted transmit power and channel capacity. Compared with other mechanisms, the quantum black-winged kite mechanism can optimize resource allocation to enable the system to reach the optimal state in different scenarios, and has a wide range of applications. Attached Figure Description

[0046] Figure 1 This is a flowchart of a Massive MIMO resource allocation method for a single-bit smart reflector according to an embodiment of the present invention;

[0047] Figure 2 In the embodiments of the present invention, the user expects to be and A comparison of convergence curves for various optimization methods under different conditions;

[0048] Figure 3 In the embodiments of the present invention, the user expects to be and A comparison of convergence curves for various optimization methods under different conditions;

[0049] Figure 4 In the embodiments of the present invention, the user expects to be and A comparison of convergence curves for various optimization methods under different conditions;

[0050] Figure 5 In the embodiments of the present invention, the user expects to be and A comparison of convergence curves for various optimization methods under different conditions;

[0051] Figure 6 In the embodiments of the present invention, the user expects to be and A comparison of convergence curves for various optimization methods under different conditions. Detailed Implementation

[0052] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0053] Specific Implementation Plan 1: Combining Figure 1As shown, this invention provides a method for allocating resources for Massive MIMO with a single-bit smart reflector, comprising the following steps:

[0054] S100. Establish a Massive MIMO wireless communication system model based on a single-bit intelligent reflector, and set the constraints and objective function;

[0055] include,

[0056] The system model includes one base station, two smart reflectors, and one user. All smart reflectors in this system model are passive single-bit smart reflectors. Both the base station and the smart reflectors use multiple-input multiple-output antennas for signal transmission and reception. The smart reflectors are deployed between the base station and the user. The reflector element coefficients and phase angles of the smart reflectors are dynamically adjusted by the central controller to obtain suitable reflected signals.

[0057] Base station has One antenna, user has Each antenna, and both smart reflectors have There are one reflection unit, and the reflection matrices of the two intelligent reflection surfaces are respectively... and ,in This represents a diagonal matrix formed by the elements of the set in sequence. For the first The first intelligent reflective surface The coefficient of each reflecting unit, For the first The first intelligent reflective surface The phase of each reflecting unit, , The imaginary unit, For the first The first intelligent reflective surface Phase angle of each reflecting unit, , For the two discrete phase angles required, , , and These are the sets of phase angles for the first and second intelligent reflective surfaces, respectively. , ;

[0058] Let the base station transmit a unit matrix. The transmit power matrix of the transmitting antenna is denoted as ,in For the first The transmit power of the root transmitting antenna, , and These represent the maximum and minimum allowable transmit power for each transmitting antenna, respectively. The user received signal matrix is ​​represented as follows: The channel state information from the base station to the first smart reflector is represented as follows: The channel state information from the first intelligent reflector to the user is represented as: The channel state information from the first smart reflector to the second smart reflector is represented as follows: The channel state information from the second intelligent reflector to the user is represented as follows: Due to the influence of distance and object obstruction, there is no direct path between the base station, the user, and the second smart reflector.

[0059] Assume that the noise received at both smart reflectors and the user is complex white Gaussian noise, expressed as follows: , and Therefore, the user's received signal can be represented as ,in The signal transmitted by the base station received by the user. The noise signal received at the first smart reflector is the noise signal transmitted to the user. The noise signal from the received noise at the second smart reflector to the user; the channel capacity is expressed as... ,in This indicates finding the value of the determinant of a matrix. Represented as An identity matrix of dimension 1 The signal power matrix from the base station to the user after passing through the first smart reflector. This indicates finding the conjugate transpose of a matrix. The signal power matrix for the base station transmitting signals through the first and second smart reflectors to the user. For the user's noise power matrix, The noise power matrix from the received noise at the first smart reflector to the user is given. The noise power matrix from the received noise at the first smart reflector to the user via the first and second smart reflectors is given. The noise power matrix from the received noise at the second smart reflector to the user is given. , and These are the noise power figures at the first smart reflector, the second smart reflector, and the user's location, respectively.

[0060] By jointly optimizing the transmit power of the transmitting antenna and the phase angle of the reflecting elements in the two smart reflectors, range constraints are imposed on the transmit power of the transmitting antenna and the channel capacity. A target function is then formulated through normalization and weighting. Therefore, the target function is set as follows: ,in , , The summation symbol is used to represent the summation symbol. The symbol represents the absolute value. , and For user-expected parameters, and These are the maximum and minimum values ​​of the required channel capacity;

[0061] S200. Set relevant parameters and initialize the quantum position of each quantum black-winged kite. Measure the initial quantum position of each quantum black-winged kite through the measurement equation. Map it to the corresponding method according to the mapping rule and substitute it into the wireless communication system model. Set a fitness function with penalty and calculate the fitness value of all initial positions. Determine the local optimal quantum position, the local optimal measurement position, the global optimal quantum position, and the global optimal measurement position.

[0062] include,

[0063] Assume it exists Quantum Black-winged Kite, each Quantum Black-winged Kite possesses Dimension, among which To find the maximum dimension of the solution space, Each quantum black-winged kite has its own quantum position, then the first... The generation The quantum position of the quantum black-winged kite is ,in , For the number of iterations, The maximum number of iterations, , First generation of seasons , The quantum position of the quantum black-winged kite All dimensions are random numbers uniformly distributed between [0,1]. Dimensions set as Then the first The set of quantum positions of all quantum black-winged kites is ;

[0064] The first was obtained through measurement. The generation The measurement position of the quantum black-winged kite is , No. The generation Quantum Black-winged Kite The equation for measuring a quantum position is: ,in A random number that follows a uniform distribution between [0,1]. , ;Will Mapped to resource allocation method according to mapping rules. , , And substitute it into the wireless communication system model, where the mapping rule is: , The first The generation The transmission power of the antenna chosen by the quantum black-winged kite and the phase angle of the smart reflector, Represented as the first The generation The quantum black-winged kite's chosen first The transmit power of the root transmitting antenna, Represented as the first The generation The quantum black-winged kite's chosen first One phase angle, For the two fixed discrete phase angles to be selected, , ;

[0065] The penalty function is set according to the constraints. ,in

[0066] The penalty coefficient is used to calculate the first... The generation The quantum black-winged kite has a penalty fitness value. , ; will the first Quantum Black-winged Kite as of the [number]th The quantum position with the smallest fitness value up to date and the measurement position are denoted as the nth quantum position. Local optimal quantum position of the quantum black-winged kite and local optimal measurement location All quantum black-winged kites up to the [number]th The quantum position with the minimum fitness value and the measured position are denoted as the global optimal quantum position. and the global optimal measurement location ;

[0067] S300: Each quantum black-winged kite updates its quantum position and measurement position according to the attack behavior, maps it to the corresponding method according to the mapping rule and substitutes it into the wireless communication system model, calculates the corresponding fitness value, and updates the local optimal quantum position, local optimal measurement position, global optimal quantum position and global optimal measurement position.

[0068] include,

[0069] Set a selection probability Then, random numbers uniformly distributed between [0,1] are generated. ,in , ;

[0070] like Then define the number of attacks performed after the attack is executed. The generation Quantum Black-winged Kite The quantum rotation angle is ,in , , , , and All are random numbers that follow a uniform distribution between [0,1]. and For the first Quantum Black-winged Kite The local optimal quantum position and the local optimal measurement position of dimensionality. and For the first Global optimal quantum position and global optimal measurement position. , , , and All are constant parameters. , ;like Then define the number of attacks performed after the attack is executed. The generation Quantum Black-winged Kite The quantum rotation angle is ,in , , , ;

[0071] After defining the execution of the attack behavior, the first The generation Quantum Black-winged Kite Quantum position The evolutionary method is as follows: ,in A random number that follows a uniform distribution between [0,1]. The value represents the probability that a qubit will mutate when the quantum rotation angle is 0. constants between , ;

[0072] After obtaining the execution of the attack, the first The generation The quantum position of the quantum black-winged kite is ,in Then update the number of attacks executed. The set of quantum positions of the quantum black-winged kite is ; The measurement of the number of seconds after the attack was executed The generation The measurement position of the quantum black-winged kite is After executing the attack, the first The generation Quantum Black-winged Kite The equation for measuring a quantum position is: ,in A random number that follows a uniform distribution between [0,1]. , ;

[0073] Will Mapped to resource allocation method according to mapping rules. , And substitute it into the wireless communication system model to calculate the number of seconds after the attack is executed. The generation The quantum black-winged kite has a fitness value with a penalty. The mapping rule is as follows: , Each is the 1st time after the attack is executed The generation The transmission power of the antenna chosen by the quantum black-winged kite and the phase angle of the smart reflector, This indicates the number of times the attack has been performed. The generation The quantum black-winged kite's chosen first The transmit power of the root transmitting antenna, This indicates the number of times the attack has been performed. The generation The quantum black-winged kite's chosen first One phase angle, , ; will the first Local optimal quantum position of the quantum black-winged kite and local optimal measurement location Updated to execute attack behavior up to the [number]th Replace the quantum position with the minimum fitness value and the measurement position with the globally optimal quantum position. and the global optimal measurement location Updated to include all Quantum Blackwing Kite units up to the date of the attack. The quantum position with the smallest fitness value and the measurement position;

[0074] S400: After each quantum black-winged kite performs an attack, it updates its quantum position and measurement position based on its migration behavior. It then maps these positions to the corresponding methods according to the mapping rules and substitutes them into the wireless communication system model. The corresponding fitness values ​​are calculated, and the local optimal quantum position, local optimal measurement position, global optimal quantum position, and global optimal measurement position are updated again.

[0075] Including, if Define the number of migration actions performed after the migration is executed. The generation Quantum Black-winged Kite The quantum rotation angle is ,in This indicates the number of times the attack was performed. Randomly select the number from other quantum black-winged kites. The measurement location of the quantum black-winged kite. , , and All are random numbers that follow a uniform distribution between [0,1]. and To be the first after the attack is executed The generation Quantum Black-winged Kite The quantum position and measurement position of a dimension. and To be the first after the attack is executed The generation Quantum Black-winged Kite The local optimal quantum position and the local optimal measurement position of dimensionality. and To be the first after the attack is executed The generation Global optimal quantum position and global optimal measurement position. are constant parameters. , ;like Define the number of migration actions performed after the migration is executed. The generation Quantum Black-winged Kite The quantum rotation angle is ,in and To be the first after the attack is executed Randomly select the number from other quantum black-winged kites. and No. The quantum position of the black-winged kite in dimensionality. and To be the first after the attack is executed Randomly select the number from other quantum black-winged kites. and No. The quantum black-winged kite's measurement position. A random number that follows a uniform distribution between [0,1]. Randomly select from 0 and 1. , ;

[0076] Define the first step after executing the migration behavior The generation Quantum Black-winged Kite The evolution method of quantum position is as follows ,in A random number that follows a uniform distribution between [0,1]. The value represents the probability that a qubit will mutate when the quantum rotation angle is 0. constants between , ;

[0077] After obtaining the execution of the migration action, the first The generation The quantum position of the black-winged kite is ,in Then update the migration action after it is performed. The quantum position set of the black-winged kite is ; The measurement of the number of days after the migration was carried out The generation The quantum black-winged kite measured the position as follows: After the migration was carried out The generation Quantum Black-winged Kite The equation for measuring a quantum position is: ,in A random number that follows a uniform distribution between [0,1]. , ;

[0078] Will Mapping to a resource allocation method according to the mapping rules in step S200. Substitute it into the wireless communication system model to calculate the number of steps after the migration behavior is executed. The generation The quantum black-winged kite has a fitness value with a penalty. , ; will the first Local optimal quantum position of the quantum black-winged kite and local optimal measurement location Updated again to indicate that after the execution of the migration, up to the [number]th [year] Replace the quantum position with the minimum fitness value and the measurement position with the globally optimal quantum position. and the global optimal measurement location Updated again to include all quantum black-winged kites after the migration process, up to the [number missing]. The quantum position with the smallest fitness value and the measurement position;

[0079] S500. Determine whether the search mechanism has reached the maximum number of iterations. If yes, output the globally optimal measurement position; otherwise, let... Return to step S300 and continue iterating;

[0080] S600. Map the obtained global optimal measurement position according to the mapping rules in step S200 to obtain an economical and efficient resource allocation method.

[0081] Specific Implementation Scheme 2: This invention provides a single-bit intelligent reflector Massive MIMO resource allocation system. This system has a program module corresponding to the above steps, and executes the steps in the above-described single-bit intelligent reflector Massive MIMO resource allocation method when running.

[0082] The other combinations and connections in this implementation scheme are the same as in Specific Implementation Scheme 1.

[0083] Specific Implementation Scheme 3: The present invention provides a computer-readable storage medium storing a computer program configured to implement, when called by a processor, the steps of a Massive MIMO resource allocation method for a single-bit smart reflector.

[0084] The other combinations and connections in this implementation scheme are the same as in Specific Implementation Scheme 1.

[0085] Simulation Experiment

[0086] For ease of description, the resource allocation method of the quantum black-winged kite mechanism proposed in this invention is abbreviated as QBKA. The search mechanisms used for comparison are the black-winged kite search mechanism, the quantum butterfly mechanism, and the improved particle swarm mechanism, which are abbreviated as BKA, QHD, and IPO-PSO, respectively.

[0087] To comprehensively compare the performance of the four methods, QBKA, BKA, QHD, and IPO-PSO were initialized identically, and the same Massive MIMO wireless communication system model parameters were set: The base station coordinates are assumed to be located at (0,0)m, and the number of antennas is... The maximum and minimum allowable transmit power for each base station antenna are respectively and The user's coordinates are located at (250,0) m, and the number of antennas is... The first smart reflector is located at coordinates (100, 110) m, and the second smart reflector is located at coordinates (200, 60) m. Both smart reflectors have reflective elements... The reflection coefficient of each reflecting element is 1; the maximum and minimum allowable channel capacity are respectively... and The information transmission between the base station and the first smart reflector, between the first smart reflector and the second smart reflector, and between the two smart reflectors and the user are all line-of-sight transmissions, with a Rice factor of 1 and a channel fading coefficient of 2.2. The noise power spectral density at the two smart reflectors and the user is -150dBm / Hz, and for ease of operation, the system bandwidth is taken as 1Hz.

[0088] Setting up in QBKA , , , , , , , , , , The four mechanisms all have the same population size and maximum number of iterations, which are respectively... and The fitness curve is plotted using the average fitness value from 100 runs. Other relevant parameter settings for BKA can be found in Jun Wang et al.'s article "Black-winged kite algorithm: a nature-inspired meta-heuristic for solving benchmark functions and engineering problems" published in *Artificial Intelligence Review*. Other relevant parameter settings for QHD can be found in Zhao Lishuai et al.'s article "A Resource Allocation Method for Intelligent Reflector Massive MIMO Systems". Other relevant parameter settings for IPO-PSO can be found in Hui Xianyang et al.'s article "A Communication Anti-interference Decision Engine Based on Initial Population Optimization Particle Swarm Mechanism" published in *Communication Technology*.

[0089] from Figure 2 It can be seen that when the user expects to be and At this point, the constraint function will have some influence on discrete factors, but within the constraint range, only continuous variables determine the fitness value. In this case, BKA performs well, but QBKA clearly achieves even better performance, overcoming the performance limitation of BKA, which is prone to getting trapped in local optima. Figures 3 to 6 As can be seen, the fitness value is determined by both continuous and discrete variables. In this case, BKA performs the worst, while QBKA is the optimal strategy. QBKA overcomes the limitations of BKA in solving discrete and mixed optimization problems and possesses better global optimization and global convergence capabilities. In summary, QBKA outperforms BKA under all user expectation allocation conditions. It can find an economical and efficient resource allocation method under constraints on transmit antenna power and channel capacity, achieving the balance required by users through weighting and normalization.

[0090] While the present invention has been disclosed above, its scope of protection is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and all such changes and modifications will fall within the scope of protection of the present invention.

Claims

1. A method for allocating resources for Massive MIMO with a single-bit smart reflector, characterized in that, Includes the following steps: S100. Establish a system model including base stations, smart reflectors, and users. By jointly optimizing the transmit power of the transmit antenna and the phase angle of the reflector element in the smart reflector, range constraints are imposed on the transmit power of the transmit antenna and the channel capacity. The objective function is formulated through normalization and weighted processing. S200: Set the parameters of the quantum black-winged kite mechanism and initialize the quantum position of each quantum black-winged kite. Measure the initial quantum position of each quantum black-winged kite through the measurement equation to obtain the initial measurement position of each quantum black-winged kite. Map the initial measurement position of each quantum black-winged kite to a resource allocation method according to the mapping rule and substitute it into the system model of step S100. Set a fitness function with penalty according to the constraint conditions and calculate the fitness values ​​of all initial positions to determine the local optimal quantum position, the local optimal measurement position, the global optimal quantum position, and the global optimal measurement position. S300. Each quantum black-winged kite updates its quantum position and measurement position according to the attack behavior. According to the mapping rule, each quantum black-winged kite after the attack behavior is mapped to a resource allocation method and substituted into the system model of step S100. The corresponding fitness value is calculated, and the local optimal quantum position, local optimal measurement position, global optimal quantum position and global optimal measurement position are updated again. S400. After each quantum black-winged kite performs the attack behavior in step S300, its quantum position and measurement position are updated according to the migration behavior. According to the mapping rule, each quantum black-winged kite after performing the migration behavior is mapped to a resource allocation method and substituted into the system model in step S100. The corresponding fitness value is calculated, and the local optimal quantum position, local optimal measurement position, global optimal quantum position, and global optimal measurement position are updated again. S500: Determine whether the search mechanism has reached the maximum number of iterations. If yes, output the globally optimal measurement position; otherwise, return to step S300 and continue iterating until the maximum number of iterations is reached and the globally optimal measurement position is output. S600: Map the globally optimal measurement position obtained in step S500 according to the mapping rules in step S200 to obtain an economical and efficient resource allocation method.

2. The method for allocating resources for a single-bit smart reflector in Massive MIMO according to claim 1, characterized in that: In step S100, the following are included: Assume the base station has There are one antenna, and the user has... One antenna, both smart reflectors have There are one reflection unit, and the reflection matrices of the two intelligent reflection surfaces are respectively... and ,in This represents a diagonal matrix formed by the elements of the set in sequence. For the first The first intelligent reflective surface The coefficient of each reflecting unit, For the first The first intelligent reflective surface The phase of each reflecting unit, , The imaginary unit, For the first The first intelligent reflective surface Phase angle of each reflecting unit, , For the two discrete phase angles required, , , and These are the sets of phase angles for the first and second intelligent reflective surfaces, respectively. , ; Let the base station transmit a unit matrix. The transmit power matrix of the transmitting antenna is denoted as ,in For the first The transmit power of the root transmitting antenna, , and These represent the maximum and minimum allowable transmit power for each transmitting antenna, respectively. The user received signal matrix is ​​represented as follows: The channel state information from the base station to the first smart reflector is represented as follows: The channel state information from the first intelligent reflector to the user is represented as: The channel state information from the first smart reflector to the second smart reflector is represented as follows: The channel state information from the second intelligent reflector to the user is represented as follows: ; Assume that the noise received at both smart reflectors and the user terminal is complex Gaussian white noise, expressed as follows: , and Therefore, the user's received signal can be represented as ,in The signal transmitted by the base station received by the user. The noise signal received at the first smart reflector is the noise signal transmitted to the user. The noise signal from the received noise at the second smart reflector to the user; the channel capacity is expressed as... ,in This indicates finding the value of the determinant of a matrix. Represented as An identity matrix of dimension 1 The signal power matrix from the base station to the user after passing through the first smart reflector. This indicates finding the conjugate transpose of a matrix. The signal power matrix for the base station transmitting signals through the first and second smart reflectors to the user. For the user's noise power matrix, The noise power matrix from the received noise at the first smart reflector to the user is given. The noise power matrix from the received noise at the first smart reflector to the user via the first and second smart reflectors is given. The noise power matrix from the received noise at the second smart reflector to the user is given. , and These are the noise power figures at the first smart reflector, the second smart reflector, and the user's location, respectively. Let the objective function be ,in , , The summation symbol is used to represent the summation symbol. The symbol represents the absolute value. , and For user-expected parameters, and These represent the maximum and minimum values ​​of the required channel capacity.

3. The method for allocating Massive MIMO resources using a single-bit smart reflector according to claim 2, characterized in that: Step S200 includes, Suppose there exists Quantum Black-winged Kite, each Quantum Black-winged Kite has dimension, ;No. The generation The quantum position of the quantum black-winged kite is ,in , For the number of iterations, The maximum number of iterations, , First generation of seasons , The quantum position of the quantum black-winged kite All dimensions are random numbers uniformly distributed between [0,1]. Dimensions set as Then the first The set of quantum positions of all quantum black-winged kites is ; The first was obtained through measurement. The generation The measurement position of the quantum black-winged kite is , No. The generation Quantum Black-winged Kite The equation for measuring a quantum position is: ,in The random numbers are uniformly distributed between [0,1]. Mapped to resource allocation method according to mapping rules. , , And substitute it into the system model, where the mapping rule is: , The first The generation The transmission power of the antenna chosen by the quantum black-winged kite and the phase angle of the smart reflector, Represented as the first The generation The quantum black-winged kite's chosen first The transmit power of the root transmitting antenna, Represented as the first The generation The quantum black-winged kite's chosen first One phase angle, The two fixed discrete phase angles are provided for selection; The penalty function is set according to the constraints. ,in The penalty coefficient is used to calculate the first... The generation The quantum black-winged kite has a penalty fitness value. ; will the first Quantum Black-winged Kite as of the [number]th The quantum position with the smallest fitness value up to date and the measurement position are denoted as the nth quantum position. Local optimal quantum position of the quantum black-winged kite and local optimal measurement location All quantum black-winged kites up to the [number]th The quantum position with the minimum fitness value and the measured position are denoted as the global optimal quantum position. and the global optimal measurement location .

4. The method for allocating Massive MIMO resources using a single-bit smart reflector according to claim 3, characterized in that: Step S300 includes, Set a selection probability Then, random numbers uniformly distributed between [0,1] are generated. ,in ; like Define the first attack action after execution. The generation Quantum Black-winged Kite The quantum rotation angle is ,in , , , , and All are random numbers that follow a uniform distribution between [0,1]. and For the first Quantum Black-winged Kite The local optimal quantum position and the local optimal measurement position of dimensionality. and For the first Global optimal quantum position and global optimal measurement position. , , , and All are constant parameters; like Define the first attack action after execution. The generation Quantum Black-winged Kite The quantum rotation angle is ,in , ; After defining the execution of the attack behavior, the first The generation Quantum Black-winged Kite Quantum position The evolutionary method is as follows: ,in A random number that follows a uniform distribution between [0,1]. This represents the probability that a qubit will mutate when the quantum rotation angle is 0; its value is... The constants between; After obtaining the execution of the attack behavior, the first The generation The quantum position of the quantum black-winged kite is Then update the number of attacks executed after the attack is performed. The set of quantum positions of the quantum black-winged kite is ; The measurement of the number of seconds after the execution of the attack behavior The generation The measurement position of the quantum black-winged kite is After executing the attack, the first The generation Quantum Black-winged Kite The equation for measuring a quantum position is: ,in A random number that follows a uniform distribution between [0,1]. Will Mapped to resource allocation method according to mapping rules. Substituting this into the wireless communication system model, the calculation is performed after the attack is executed. The generation The quantum black-winged kite has a penalty fitness value. The mapping rule is as follows: , Each is the 1st time after the attack is executed The generation The transmission power of the antenna chosen by the quantum black-winged kite and the phase angle of the smart reflector, This indicates the number of times the attack has been performed. The generation The quantum black-winged kite's chosen first The transmit power of the root transmitting antenna, This indicates the number of times the attack has been performed. The generation The quantum black-winged kite's chosen first The phase angle; the first phase angle; Local optimal quantum position of the quantum black-winged kite and local optimal measurement location Updated to execute attack behavior up to the [number]th Replace the quantum position with the minimum fitness value and the measurement position with the globally optimal quantum position. and the global optimal measurement location Updated to include all Quantum Blackwing Kite units up to the date of the attack. The quantum position and measurement position with the smallest fitness value up to date.

5. The method for allocating Massive MIMO resources using a single-bit smart reflector according to claim 4, characterized in that: In step S400, the following are included: like Define the number of migration actions performed after the migration is executed. The generation Quantum Black-winged Kite The quantum rotation angle is ,in This indicates the number of times the attack was performed. Randomly select the number from other quantum black-winged kites. The measurement location of the quantum black-winged kite. , , and All are random numbers that follow a uniform distribution between [0,1]. and To be the first after the attack is executed The generation Quantum Black-winged Kite The quantum position and measurement position of a dimension. and To be the first after the attack is executed The generation Quantum Black-winged Kite The local optimal quantum position and the local optimal measurement position of dimensionality. and To be the first after the attack is executed The generation Global optimal quantum position and global optimal measurement position. These are constant parameters; like Define the number of migration actions performed after the migration is executed. The generation Quantum Black-winged Kite The quantum rotation angle is ,in and To be the first after the attack is executed Randomly select the number from other quantum black-winged kites. and No. The quantum position of the black-winged kite in dimensionality. and To be the first after the attack is executed Randomly select the number from other quantum black-winged kites. and No. The quantum black-winged kite's measurement position. A random number that follows a uniform distribution between [0,1]. Randomly select from 0 and 1; Define the first step after executing the migration behavior The generation Quantum Black-winged Kite The evolution method of quantum position is as follows ,in A random number that follows a uniform distribution between [0,1]. This represents the probability that a qubit will mutate when the quantum rotation angle is 0; its value is... The constants between; After obtaining the execution of the migration action, the first The generation The quantum position of the black-winged kite is Then update the migration behavior after it is executed. The quantum position set of the black-winged kite is ; The measurement of the number of days after the migration was carried out The generation The quantum black-winged kite measured the position as follows: After the migration was carried out The generation Quantum Black-winged Kite The equation for measuring a quantum position is: ,in A random number that follows a uniform distribution between [0,1]. Will Mapping to a resource allocation method according to the mapping rules in step S200. Substitute it into the wireless communication system model to calculate the number of steps after the migration behavior is executed. The generation The quantum black-winged kite has a penalty fitness value. ; will the first Local optimal quantum position of the quantum black-winged kite and local optimal measurement location Updated again to indicate that after the execution of the migration, up to the [number]th [year] Replace the quantum position with the minimum fitness value and the measurement position with the globally optimal quantum position. and the global optimal measurement location Updated again to include all quantum black-winged kites after the migration process, up to the [number missing]. The quantum position and measurement position with the smallest fitness value up to date.

6. A Massive MIMO resource allocation system with a single-bit intelligent reflector, characterized in that: The system has a program module corresponding to the steps of any one of claims 1-5 above, and executes the steps in the above-described method for allocating resources of a single-bit intelligent reflector in Massive MIMO when running.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program configured to, when invoked by a processor, implement the steps of the Massive MIMO resource allocation method for a single-bit smart reflector as described in any one of claims 1-5.

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