阅读说明：本技术 一种安全Massive MIMO网络资源配置方法 (Safe Massive MIMO network resource allocation method ) 是由 苏雨萌 高洪元 张世铂 陈梦晗 杜亚男 周晓琦 于 2021-09-08 设计创作，主要内容包括：本发明公开了一种安全Massive MIMO网络资源配置方法,建立通信系统模型；初始化量子秃鹰群,经映射规则得到量子秃鹰的位置并计算适应度,选出全局最优量子位置；根据搜索演化规则更新量子秃鹰群量子位置；通过盘旋演化对量子秃鹰群量子位置进行更新；根据俯冲规则更新量子秃鹰群量子位置,经映射规则得到更新后量子秃鹰位置并计算适应度,更新量子秃鹰群全局最优量子位置；如果迭代次数小于最大迭代次数,返回步骤三；否则,迭代终止,输出量子秃鹰群全局最优量子位置,根据映射规则得到位置并得到网络资源配置方案。本发明提高系统的资源利用率,极大地提升Massive MIMO系统的保密容量。(The invention discloses a safe Massive MIMO network resource allocation method, which comprises the steps of establishing a communication system model; initializing a quantum bald eagle group, obtaining the position of the quantum bald eagle through a mapping rule, calculating the fitness, and selecting the global optimal quantum position; updating the quantum position of the quantum bald eagle group according to a search evolution rule; updating the quantum position of the quantum bald eagle group through spiral evolution; updating the quantum position of the quantum bald eagle group according to a dive rule, obtaining the updated quantum bald eagle position through a mapping rule, calculating the fitness, and updating the global optimal quantum position of the quantum bald eagle group; if the iteration times are less than the maximum iteration times, returning to the step three; otherwise, iteration is terminated, the globally optimal quantum position of the quantum bald eagle cluster is output, the position is obtained according to the mapping rule, and a network resource allocation scheme is obtained. The invention improves the resource utilization rate of the system and greatly improves the secrecy capacity of the Massive MIMO system.)

1. A safe Massive MIMO network resource allocation method is characterized by comprising the following steps:

establishing a safe Massive MIMO communication system model based on an intelligent reflector;

initializing a quantum bald eagle group, obtaining the position of the quantum bald eagle through a mapping rule, calculating the fitness, and selecting the global optimal quantum position by comparing the fitness;

step three, updating the quantum position of the quantum bald eagle group according to the search evolution rule;

step four, updating the quantum position of the quantum bald eagle group through spiral evolution;

step five, updating the quantum position of the quantum bald eagle group according to the dive rule, obtaining the updated quantum bald eagle position through the mapping rule, calculating the fitness, and updating the global optimal quantum position of the quantum bald eagle group;

step six, if the iteration times are less than the preset maximum iteration times, making t equal to t +1, and returning to the step three; otherwise, iteration is terminated, the globally optimal quantum position of the quantum bald eagle group is output, the position is obtained according to the mapping rule, and then the safe Massive MIMO network resource allocation scheme based on the intelligent reflecting surface is obtained.

2. The method for configuring secure Massive MIMO network resources according to claim 1, wherein: step one, establishing a safe Massive MIMO communication system model based on an intelligent reflector specifically comprises the following steps:

the communication system comprises a base station configured with M antennas, receiving ends configured with N antennas, a single-antenna eavesdropper, a central controller and an intelligent reflecting surface comprising L reflecting elements, wherein the central controller adjusts the phase shift of each reflecting element in real time, and the phase shift decision result forms a phase shift matrix phi of the intelligent reflecting surface:wherein diag { } denotes a diagonal matrix,θ_ithe phase shift control angle matrix of all the reflection elements in the intelligent reflection surface can be expressed as theta ═ theta ·, where i is 1,2₁,θ₂,...,θ_L]； Respectively representing the channel state information from the base station to the receiving end, the channel state information from the base station to the intelligent reflecting surface and the channel state information from the intelligent reflecting surface to the receiving end,andrepresenting base station to eavesdropper channel state information and intelligent reflector to eavesdropper channel stateInformation;

definition ofA diagonal matrix of base station transmission power, where p_mWhich represents the transmission power of the mth antenna of the base station, M1, 2. By usingThe signal which represents the unit energy signal sent by the base station and is received by the receiving end after the cooperative transmission of the intelligent reflecting surface isy_rec＝(G_{BS_rec}+G_{IR_rec}·Φ·G_{BS_IR})·P·x+G_{IR_rec}·Φ·n_IRS+n′_recWhereinAndrespectively representing complex white Gaussian noise at the intelligent reflecting surface and the receiving end; the eavesdropper receives as the signalWhereinRepresenting white gaussian noise at the eavesdropper; under the scene of intelligent reflector cooperative communication, the information transmission rate of a receiving end is as follows:

where det (.) represents the value of determinant of matrix, I_NRepresents an N × N dimensional identity matrix, ()^HWhich represents the transpose of the conjugate,w₁and w₂Respectively representing the noise power at the intelligent reflecting surface and the receiving end;

the rate at which an eavesdropper leaks information is:

whereinw₃Representing the noise power at the eavesdropper, the secret capacity of a secure Massive MIMO system isWhereinRepresents taking the maximum function;

aiming at a safe Massive MIMO communication system based on an intelligent reflector, a network resource allocation problem with the maximization of secret capacity as an optimization target is providedThe constraint conditions are as follows: theta is not less than 0_i＜2π，i＝1,2,...,L，0＜p_m≤p_maxM1, 2, M, wherein p_maxRepresenting the maximum power each base station antenna is allowed to transmit.

3. The method for configuring secure Massive MIMO network resources according to claim 1, wherein: initializing the quantum bald eagle group, obtaining the position of the quantum bald eagle through a mapping rule, calculating the fitness, and selecting the global optimal quantum position by comparing the fitness specifically as follows:

the quantum bald eagle group consists of K quantum bald eaglesThe search space dimension of each quantum bald eagle is D, which represents the dimension of the problem to be solved; the quantum position of the kth quantum bald eagle is shown asWhereinK1, 2,., K, D1, 2, ·, D; obtaining the position of the kth quantum bald eagle according to the mapping ruleThe specific rule is as follows:whereinAndrepresenting the upper and lower bounds of the d-dimension search interval of the Quantum bald eagle; the position of each quantum balde hawk corresponds to a resource allocation scheme based on an intelligent reflecting surface safety Massive MIMO network; by a fitness functionCalculating the fitness of the kth quantum bald eagle position in the tth generation, wherein the value of the kth quantum bald eagle position also represents the fitness of the corresponding kth quantum bald eagle quantum position in the tth generation; recording the quantum position of the quantum bald hawk with the maximum fitness till the t-th iteration as the global optimal quantum position

4. The method for configuring secure Massive MIMO network resources according to claim 1, wherein: step three, updating the quantum position of the quantum bald eagle group according to the search evolution rule specifically comprises the following steps:

defining front delta K quantum bald hawks with better fitness in the quantum bald hawk group as excellent individuals, wherein delta represents the ratio of the excellent individuals,for the average quantum position of excellent individuals, the updating equation of the d-dimension quantum rotation angle and quantum position of the kth quantum bald eagle in the search stage is as follows:andwherein e₁And e₂The influence factor represents the influence degree of excellent individual and global optimal quantum positions on the quantum rotation angle in the search stage,andabs (.) represents the absolute value of the random number that follows a standard normal distribution; obtaining the updated position of the quantum bald eagle through the mapping rule, and then calculating the fitness of the quantum bald eagle; by comparing the quantum position of the kth quantum bald eagle before the updating of the search stageAnd the updated quantum position of the search stageThe degree of adaptability is as followsIs more thanThe degree of adaptability ofOtherwiseHeld constant, K ═ 1, 2.., K; selecting the quantum position with the maximum fitness after the updating of the search stage as the global optimal quantum position after the search stage of the quantum bald eagle groupAfter the search phase was completed, the mean quantum positions of the first delta. K excellent individuals were recorded

5. The method for configuring secure Massive MIMO network resources according to claim 1, wherein: step four, through spiral evolution, the updating of the quantum position of the quantum bald eagle group specifically comprises the following steps:

in the population circling stage, the quantum bald eagle group flies spirally in a selected search interval to search for a prey, and the updating equation of the d-dimension quantum rotation angle and the quantum position of the kth quantum bald eagle in the circling stage is as follows:andwherein e₃And e₄Is an influencing factor;representing the adjacent individual number of the kth quantum bald hawk; is [0,2 π ]]The number of the uniform random numbers in the space,the delta is a control factor which is the sum of the control factors,is [0,1 ]]A uniform random number in between; updating the position of the quantum bald hawk according to the mapping rule and calculating the fitness; by comparing the quantum position of the kth quantum bald eagle before the spiral evolution updateAnd updated quantum positionThe degree of fitness of (1) is as followsIs more thanThe degree of adaptability ofOtherwiseHeld constant, K ═ 1, 2.., K; selecting the quantum position with the maximum fitness after the circle evolution stage is updated as the global optimal quantum position after the circle stage of the quantum bald eagle groupMean qubits of the first delta K excellent individuals after the update of the disclination stagePut as

6. The method for configuring secure Massive MIMO network resources according to claim 1, wherein: fifthly, updating the quantum position of the quantum bald eagle group according to the dive rule, obtaining the updated quantum bald eagle position through the mapping rule and calculating the fitness, wherein the updating of the global optimal quantum position of the quantum bald eagle group specifically comprises the following steps:

in the population dive stage, the quantum bald eagle flies to a target prey in a quick dive mode according to the information in the search and hover stages, and the updating equation of the d-dimension quantum rotation angle and the quantum position of the kth quantum bald eagle in the dive stage is as follows:andwherein e₅And e₆The influence factor represents the influence degree of excellent individual and global optimal quantum positions on the quantum rotation angle in the diving stage; is [0,2 π ]]The number of the uniform random numbers in the space,obtaining the updated position of the quantum bald hawk according to the mapping ruleAnd calculating the fitness, if the quantum position of the quantum bald hawk is updated after divingThe fitness of the quantum position is larger than that before the dive updateThe degree of adaptability ofThe change is not changed; otherwise makeThe quantum position with the maximum fitness after the depression stage updating is the global optimal quantum position after the depression stage of the bald eagle group of the quantumThe mean quantum position of the former delta K excellent individuals after the update in the dive stage is recorded as

Technical Field

The invention relates to a safe Massive MIMO network resource configuration method, in particular to a safe Massive MIMO network resource configuration method based on an intelligent reflector, and belongs to the technical field of Massive MIMO safe communication.

Background

The Massive MIMO technology can serve a data transmission scene with large flow, high reliability and low delay, and is an important way for realizing future 6G communication of everything interconnection. However, with the continuous development of wireless communication technology, the access of a large number of intelligent devices brings problems of high operation cost, excessive energy consumption, uneven network coverage and the like to a communication system. As a novel cooperative communication mode, an Intelligent Reflection Surface (IRS) can adaptively adjust the phase shift of an incident signal to change the reflection transmission gain, obviously reduce the energy consumption of the traditional amplification forwarding and decoding forwarding relay cooperative mode, effectively improve the user experience of the edge of a communication network, improve the capacity and the transmission efficiency of a Massive MIMO system, and has important significance for improving the overall performance of the Massive MIMO system.

The intelligent reflecting surface is composed of a large number of low-cost passive reflecting elements, has the advantages of strong expandability, low energy consumption and the like, and in recent years, the application of the intelligent reflecting surface technology in different scenes receives more and more attention. Aiming at the Massive MIMO communication scene based on the Intelligent Reflecting Surface, a channel estimation method for Beam Training is provided by 'Terahertz Multi-User Massive MIMO With Intelligent Reflecting Surface: Beam Training and Hybrid Beam Training' published by Boyu Ning et al in IEEE Transactions on Vehicular Technology (2021, vol.70, No.2, pp.1376-1393), and the channel estimation method is analyzed aiming at the system capacity, but does not relate to the resource configuration management of the Massive MIMO network of the Intelligent Reflecting Surface, and is easy to cause resource waste. An alternative optimization mechanism is proposed for improving the Capacity of the Intelligent Reflecting Surface MIMO system by 'Capacility charateristicization for Intelligent Reflecting Surface Aided MIMO Communication' published by IEEE Journal on Selected Areas in Communications (2020, vol.38, No.8, pp.1823-1838) of Shuowen Zhang et al, but the mechanism is to configure the resources of the system under the scene of fixed reflection coefficient or fixed transmission covariance matrix of the Intelligent Reflecting Surface, limit the performance of the Intelligent Reflecting Surface, and fail to maximize the secrecy Capacity of the system, and aiming at the openness and the shareability of a wireless channel, there is a risk of being easily attacked and intercepted by other malicious devices, and the information security transmission of network users has a larger space of improvement.

At present, research on a safe Massive MIMO communication network based on an intelligent reflector is still in a starting stage, related documents are few, and a safe Massive MIMO network resource allocation method which is the same as that of the safe Massive MIMO communication network is not found through comparison with existing document retrieval.

Disclosure of Invention

Aiming at the prior art, the technical problem to be solved by the invention is to provide a safe Massive MIMO network resource configuration method, and the intelligent combined configuration of multiple resources of a Massive MIMO communication system is realized through a Quantum Bald Eagle Search (QBES) mechanism, so that the resource utilization rate of the system is improved, and the secret capacity of the Massive MIMO system is greatly improved.

In order to solve the technical problem, the invention provides a secure Massive MIMO network resource allocation method, which comprises the following steps:

establishing a safe Massive MIMO communication system model based on an intelligent reflector;

initializing a quantum bald eagle group, obtaining the position of the quantum bald eagle through a mapping rule, calculating the fitness, and selecting the global optimal quantum position by comparing the fitness;

step three, updating the quantum position of the quantum bald eagle group according to the search evolution rule;

step four, updating the quantum position of the quantum bald eagle group through spiral evolution;

step five, updating the quantum position of the quantum bald eagle group according to the dive rule, obtaining the updated quantum bald eagle position through the mapping rule, calculating the fitness, and updating the global optimal quantum position of the quantum bald eagle group;

step six, if the iteration times are less than the preset maximum iteration times, making t equal to t +1, and returning to the step three; otherwise, iteration is terminated, the globally optimal quantum position of the quantum bald eagle group is output, the position is obtained according to the mapping rule, and then the safe Massive MIMO network resource allocation scheme based on the intelligent reflecting surface is obtained.

The invention also includes:

1. establishing a safe Massive MIMO communication system model based on an intelligent reflector in the first step specifically comprises the following steps:

the communication system comprises a base station configured with M antennas, receiving ends configured with N antennas, a single-antenna eavesdropper, a central controller and an intelligent reflecting surface comprising L reflecting elements, wherein the central controller adjusts the phase shift of each reflecting element in real time, and the phase shift decision result forms a phase shift matrix phi of the intelligent reflecting surface:wherein diag { } denotes a diagonal matrix,θ_ithe phase shift control angle matrix of all the reflection elements in the intelligent reflection surface can be expressed as theta ═ theta ·, where i is 1,2₁,θ₂,...,θ_L]； Respectively representing the channel state information from the base station to the receiving end, the channel state information from the base station to the intelligent reflecting surface and the channel state information from the intelligent reflecting surface to the receiving end,andrepresenting the channel state information from the base station to the eavesdropper and the channel state information from the intelligent reflecting surface to the eavesdropper;

definition ofA diagonal matrix of base station transmission power, where p_mWhich represents the transmission power of the mth antenna of the base station, M1, 2. By usingThe signal which represents the unit energy signal sent by the base station and is received by the receiving end after the cooperative transmission of the intelligent reflecting surface isy_rec＝(G_{BS_rec}+G_{IR_rec}·Φ·G_{BS_IR})·P·x+G_{IR_rec}·Φ·n_IRS+n′_recWhereinAndrespectively representing complex white Gaussian noise at the intelligent reflecting surface and the receiving end; the eavesdropper receives as the signalWhereinRepresenting white gaussian noise at the eavesdropper; under the scene of intelligent reflector cooperative communication, the information transmission rate of a receiving end is as follows:

where det (.) represents the value of determinant of matrix, I_NRepresents an N × N dimensional identity matrix, ()^HWhich represents the transpose of the conjugate,w₁and w₂Are respectively provided withRepresenting the noise power at the intelligent reflecting surface and the receiving end;

the rate at which an eavesdropper leaks information is:

whereinw₃Representing the noise power at the eavesdropper, the secret capacity of a secure Massive MIMO system isWhereinmax { } denotes a take maximum function;

aiming at a safe Massive MIMO communication system based on an intelligent reflector, a network resource allocation problem with the maximization of secret capacity as an optimization target is providedThe constraint conditions are as follows: theta is not less than 0_i＜2π，i＝1,2,...,L，0＜p_m≤p_maxM1, 2, M, wherein p_maxRepresenting the maximum power each base station antenna is allowed to transmit.

2. Initializing a quantum bald eagle group, obtaining the position of the quantum bald eagle through a mapping rule, calculating the fitness, and selecting the global optimal quantum position by comparing the fitness:

setting a quantum bald eagle group to be composed of K quantum bald eagles, wherein the search space dimension of each quantum bald eagle is D and represents the dimension of the problem to be solved; the quantum position of the kth quantum bald eagle is shown asWherein Obtaining the position of the kth quantum bald eagle according to the mapping ruleThe specific rule is as follows:whereinAndrepresenting the upper and lower bounds of the d-dimension search interval of the Quantum bald eagle; the position of each quantum balde hawk corresponds to a resource allocation scheme based on an intelligent reflecting surface safety Massive MIMO network; by a fitness functionCalculating the fitness of the kth quantum bald eagle position in the tth generation, wherein the value of the kth quantum bald eagle position also represents the fitness of the corresponding kth quantum bald eagle quantum position in the tth generation; recording the quantum position of the quantum bald hawk with the maximum fitness till the t-th iteration as the global optimal quantum position

3. In the third step, updating the quantum position of the quantum bald eagle group according to the search evolution rule specifically comprises the following steps:

defining front delta K quantum bald hawks with better fitness in the quantum bald hawk group as excellent individuals, wherein delta represents the ratio of the excellent individuals,for the average quantum position of excellent individuals, the updating equation of the d-dimension quantum rotation angle and quantum position of the kth quantum bald eagle in the search stage is as follows:andwherein e₁And e₂The influence factor represents the influence degree of excellent individual and global optimal quantum positions on the quantum rotation angle in the search stage,andabs (.) represents the absolute value of the random number that follows a standard normal distribution; obtaining the updated position of the quantum bald eagle through the mapping rule, and then calculating the fitness of the quantum bald eagle; by comparing the quantum position of the kth quantum bald eagle before the updating of the search stageAnd the updated quantum position of the search stageThe degree of adaptability is as followsIs more thanThe degree of adaptability ofOtherwiseHeld constant, K ═ 1, 2.., K; selecting the quantum position with the maximum fitness after the updating of the search stage as the global optimal quantum position after the search stage of the quantum bald eagle groupAfter the search phase was completed, the mean quantum positions of the first delta. K excellent individuals were recorded

4. In the fourth step, through spiral evolution, the quantum position of the quantum bald eagle group is updated specifically as follows:

in the population circling stage, the quantum bald eagle group flies spirally in a selected search interval to search for a prey, and the updating equation of the d-dimension quantum rotation angle and the quantum position of the kth quantum bald eagle in the circling stage is as follows:andwherein e₃And e₄Is an influencing factor;representing the adjacent individual number of the kth quantum bald hawk; is [0,2 π ]]The number of the uniform random numbers in the space,the delta is a control factor which is the sum of the control factors,is [0,1 ]]A uniform random number in between; updating the position of the quantum bald hawk according to the mapping rule and calculating the fitness; by comparing the quantum position of the kth quantum bald eagle before the spiral evolution updateAnd updated quantum positionThe degree of fitness of (1) is as followsIs more thanThe degree of adaptability ofOtherwiseHeld constant, K ═ 1, 2.., K; selecting the quantum position with the maximum fitness after the circle evolution stage is updated as the global optimal quantum position after the circle stage of the quantum bald eagle groupThe average quantum position of the former delta K excellent individuals after the updating of the spiral evolution stage is recorded as

5. In the fifth step, the quantum position of the quantum bald eagle group is updated according to the dive rule, the updated quantum bald eagle position is obtained through the mapping rule, the fitness is calculated, and the updating of the global optimal quantum position of the quantum bald eagle group specifically comprises the following steps:

in the population dive stage, the quantum bald eagle flies to a target prey in a quick dive mode according to the information in the search and hover stages, and the updating equation of the d-dimension quantum rotation angle and the quantum position of the kth quantum bald eagle in the dive stage is as follows:andwherein e₅And e₆The influence factor represents the effect of excellent individual and global optimal quantum positions on the quantum rotation angle in the dive phaseThe degree of loudness; is [0,2 π ]]The number of the uniform random numbers in the space,obtaining the updated position of the quantum bald hawk according to the mapping ruleAnd calculating the fitness, if the quantum position of the quantum bald hawk is updated after divingThe fitness of the quantum position is larger than that before the dive updateThe degree of adaptability ofThe change is not changed; otherwise makeThe quantum position with the maximum fitness after the depression stage updating is the global optimal quantum position after the depression stage of the bald eagle group of the quantumThe mean quantum position of the former delta K excellent individuals after the update in the dive stage is recorded as

Compared with the prior art, the invention has the beneficial effects that:

(1) aiming at the defects of the existing intelligent reflector safety Massive MIMO communication system, the network resource configuration method designed by the invention can realize intelligent combined configuration of multiple resources of the Massive MIMO communication system, can improve the transmission rate of a receiver, simultaneously reduce the interception capability of an eavesdropper on useful information, reduce information leakage and obviously improve the transmission reliability and confidentiality of the Massive MIMO system.

(2) The invention can effectively solve the problems of transmission power distribution of the base station antenna of the safe Massive MIMO communication system based on the intelligent reflecting surface, phase shift control of the reflecting element and the like, can adaptively realize intelligent configuration of network resources through a quantum bald eagle search mechanism, can greatly save hardware resources, further improve the resource utilization rate of the system and further improve the overall service quality of the Massive MIMO system.

(3) The quantum bald eagle search mechanism organically integrates the thought of quantum evolution, improves the population diversity through various quantum evolution strategies, has the advantages of good convergence performance and strong global search capability, overcomes the problem that the traditional bald eagle search mechanism is easy to fall into local convergence, provides a new thought for solving other complicated engineering problems, and has good popularization.

Drawings

Fig. 1 is a schematic diagram of an intelligent reflecting surface safe Massive MIMO network resource allocation method based on a quantum balde eagle search mechanism.

Fig. 2 is a curve of the change of the secrecy capacity of the intelligent reflecting surface security Massive MIMO network resource allocation method with the number of iterations based on a quantum balde eagle search mechanism and a balde eagle search mechanism.

Fig. 3 is a curve of the secret capacity of the intelligent reflecting surface security Massive MIMO network resource allocation method based on the quantum balde eagle search mechanism and the balde eagle search mechanism, which varies with the maximum power allowed to be transmitted by the base station antenna.

Fig. 4 is a curve of secret capacity of the intelligent reflector secure Massive MIMO network resource allocation method based on a quantum balde eagle search mechanism and a balde eagle search mechanism, which varies with the number of intelligent reflectors.

Detailed Description

The invention is further described with reference to the drawings and the detailed description.

With reference to fig. 1, the intelligent reflecting surface safety Massive MIMO network resource allocation method based on the quantum bald eagle search mechanism, which is designed by the invention, comprises the following steps:

step one, establishing a safe Massive MIMO communication system model based on an intelligent reflector

Considering a safe Massive MIMO communication system based on an intelligent reflector, comprising a base station configured with M antennas, a receiving end configured with N antennas, a single-antenna eavesdropper, a central controller and the intelligent reflector comprising L reflectors, wherein the central controller can adjust the phase shift of each reflector in the intelligent reflector in real time, and the phase shift decision results form a phase shift matrix phi of the intelligent reflector, which is specifically represented as a phase shift matrix phi of the intelligent reflectorWherein diag { } denotes a diagonal matrix,θ_ithe phase shift control angle matrix of all the reflection elements in the intelligent reflection surface can be expressed as theta ═ theta ·, where i is 1,2₁,θ₂,...,θ_L]。Respectively representing the channel state information from the base station to the receiving end, the channel state information from the base station to the intelligent reflecting surface and the channel state information from the intelligent reflecting surface to the receiving end,andrepresenting the channel state information from the base station to the eavesdropper and the channel state information from the intelligent reflecting surface to the eavesdropper.

Definition ofDiagonal matrix for base station transmission powerWherein p is_mWhich represents the transmission power of the mth antenna of the base station, M1, 2. By usingThe signal which represents the unit energy signal sent by the base station and is received by the receiving end after the cooperative transmission of the intelligent reflecting surface isy_rec＝(G_{BS_rec}+G_{IR_rec}·Φ·G_{BS_IR})·P·x+G_{IR_rec}·Φ·n_IRS+n′_recWhereinAndrepresenting complex white gaussian noise at the smart reflecting surface and at the receiving end, respectively. The eavesdropper receives as the signalWhereinRepresenting white gaussian noise at the eavesdropper. Under the scene of intelligent reflector cooperative communication, the information transmission rate of a receiving end is as follows:

where det (.) represents the value of determinant of matrix, I_NRepresents an N × N dimensional identity matrix, ()^HWhich represents the transpose of the conjugate,w₁and w₂Representing the noise power at the intelligent reflecting surface and the receiving end, respectively. The rate at which an eavesdropper leaks information is:

whereinw₃Representing the noise power at the eavesdropper. Secure Massive MIMO system with a secret capacity ofWhereinmax { } denotes the take maximum function.

Aiming at a safe Massive MIMO communication system based on an intelligent reflector, the network resource allocation problem with secret capacity as an optimization target is providedThe constraint conditions are as follows: theta is not less than 0_i＜2π，i＝1,2,...,L，0＜p_m≤p_maxM1, 2, M, wherein p_maxRepresenting the maximum power each base station antenna is allowed to transmit.

Step two, initializing quantum bald eagle groups

And setting the quantum bald eagle group to be composed of K quantum bald eagles, wherein the search space dimension of each quantum bald eagle is D, and the dimension of the problem to be solved is represented. The quantum position of the kth quantum bald eagle can be expressed asWherein Obtaining the position of the kth quantum bald eagle according to the mapping ruleThe specific rule is as follows:whereinAndrepresenting the upper and lower bounds of the quantum bald eagle d-th dimension search interval. The position of each quantum balde hawk corresponds to a resource allocation scheme based on an intelligent reflecting surface safety Massive MIMO network. By a fitness functionAnd calculating the fitness of the kth quantum bald eagle position in the tth generation, wherein the value of the kth quantum bald eagle position also represents the fitness of the corresponding kth quantum bald eagle quantum position in the tth generation. Recording the quantum position of the quantum bald hawk with the maximum fitness till the t-th iteration as the global optimal quantum position

Step three, updating the quantum position of the quantum bald eagle group according to the search evolution rule

And updating the quantum position of the quantum bald eagle according to the evolution rule of the search stage. Defining front delta K quantum bald hawks with better fitness in the quantum bald hawk group as excellent individuals, wherein delta represents the ratio of the excellent individuals,is the average quantum position of excellent individuals. The updating equation of the d-dimension quantum rotation angle and the quantum position of the kth quantum bald eagle in the searching stage is as follows:andwherein e₁And e₂For influencing factors, in the search orderThe degree of influence of the excellent individual and global optimal quantum positions of the segments on the quantum rotation angle,andabs (.) represents the absolute value of the random number that follows a standard normal distribution. And obtaining the updated position of the quantum bald eagle through the mapping rule, and then calculating the fitness of the quantum bald eagle. By comparing the quantum position of the kth quantum bald eagle before the updating of the search stageAnd the updated quantum position of the search stageThe degree of adaptability is as followsIs more thanThe degree of adaptability ofOtherwiseK is kept constant, 1, 2. Selecting the quantum position with the maximum fitness after the updating of the search stage as the global optimal quantum position after the search stage of the quantum bald eagle groupAfter the search phase was completed, the mean quantum positions of the first delta. K excellent individuals were recorded

Step four, updating the quantum position of the quantum bald eagle group through spiral evolution

In the population circling stage, the quantum bald eagle group flies spirally in a selected search interval to search for a prey, and the updating equation of the d-dimension quantum rotation angle and the quantum position of the kth quantum bald eagle in the circling stage is as follows:andwherein e₃And e₄Is an influencing factor;representing the adjacent individual number of the kth quantum bald hawk; is [0,2 π ]]The number of the uniform random numbers in the space,the delta is a control factor which is the sum of the control factors,is [0,1 ]]A uniform random number in between. And updating the position of the quantum bald hawk according to the mapping rule and calculating the fitness. By comparing the quantum position of the kth quantum bald eagle before the spiral evolution updateAnd updated quantum positionThe degree of fitness of (1) is as followsIs more thanThe degree of adaptability ofOtherwiseK is kept constant, 1, 2. Selecting the quantum position with the maximum fitness after the circle evolution stage is updated as the global optimal quantum position after the circle stage of the quantum bald eagle groupThe average quantum position of the former delta K excellent individuals after the updating of the spiral evolution stage is recorded as

Step five, updating the quantum position of the quantum bald eagle group according to the dive rule

In the population dive stage, the quantum bald eagle flies to a target prey in a quick dive mode according to the information in the search and hover stages, and the updating equation of the d-dimension quantum rotation angle and the quantum position of the kth quantum bald eagle in the dive stage is as follows:andwherein e₅And e₆The influence factor represents the influence degree of excellent individual and global optimal quantum positions on the quantum rotation angle in the diving stage; is [0,2 π ]]The number of the uniform random numbers in the space,according to the mapping ruleObtaining the updated position of the quantum bald hawkAnd calculating the fitness, if the quantum position of the kth quantum bald eagle after the dive updateThe fitness of the quantum position before the dive update is larger than that of the quantum position before the dive updateThe degree of adaptability ofThe change is not changed; otherwise makeThe quantum position with the maximum fitness after the depression stage updating is the global optimal quantum position after the depression stage of the bald eagle group of the quantumThe mean quantum position of the former delta K excellent individuals after the update in the dive stage is recorded as

Step six, if the iteration times are less than the preset maximum iteration times, making t equal to t +1, and returning to the step three; otherwise, iteration is terminated, the globally optimal quantum position of the quantum bald eagle group is output, the position is obtained according to the mapping rule, and then the safe Massive MIMO network resource allocation scheme based on the intelligent reflecting surface is obtained.

The invention is further illustrated below with reference to specific parameters and simulation experiments:

for the intelligent reflector secure Massive MIMO communication system, setting M to 30, N to 20, L to 50, the coordinates of the base station to be (0,0) M, the coordinates of the receiving end to be (240,0) M, the coordinates of the intelligent reflector to be (120,90) M, and the eavesdropper to be (220, -50) M, p_max10dBm, η 1, noise power spectral densityAt-174 dBm/Hz, the system bandwidth is 10MHz, and all noise power is the same. The information transmission between the base station and the receiving end and the eavesdropper is non-line-of-sight transmission, the channel fading coefficient is 3.7, the information transmission between the intelligent reflecting surface and the base station and between the receiving end and the eavesdropper are all line-of-sight transmission, the Rice factor is 1, and the channel fading coefficient is 2.5. The parameters of the intelligent reflector safety Massive MIMO network resource configuration method based on the quantum bald eagle search mechanism are set as follows: quantum bald eagle population scale K is 10, delta is 50%, e₁＝0.5，e₂＝0.1，e₃＝0.3，e₄＝0.6，e₅＝0.6，e₆0.3, Δ 0.5, the initial quantum position of the quantum bald eagle was randomly generated within the quantum position definition domain. In order to facilitate comparison of performances of the proposed quantum balde eagle search mechanism, the balde eagle search mechanism is applied to solving a Massive MIMO network resource allocation problem for comparison, population scales of the balde eagle search mechanism and the Massive MIMO network resource allocation problem are set to be the same, the maximum iteration times are all 500 times, and all results are average values of 100 simulation experiments. Other parameter settings for bald eagle search mechanism refer to "Novel meta-ecological base area search optimization algorithm" published by H.A. Alsatar et al in Art intellectual understanding Review (2020, vol.53, pp.2237-2264).

Fig. 2 is a curve of the change of the secrecy capacity of the intelligent reflecting surface security Massive MIMO network resource allocation method with the number of iterations based on a quantum balde eagle search mechanism and a balde eagle search mechanism. From simulation results, the bald eagle search mechanism falls into local convergence at the initial stage of iteration, the quantum bald eagle search mechanism avoids the problem of falling into local convergence, the convergence speed is high, the convergence precision is high, and the global optimization capability is obviously superior to that of the bald eagle search mechanism. Under the scene that the number of base station antennas is 30 and the number of intelligent reflection elements is 50, the maximum confidentiality capacity can be obtained by the network resource allocation method based on the quantum bald eagle search mechanism.

Fig. 3 is a curve of the secrecy capacity of the intelligent reflecting surface security Massive MIMO network resource allocation method based on the quantum balde eagle search mechanism and the balde eagle search mechanism, which varies with the maximum power allowed to be transmitted by the base station antenna, and the maximum power allowed to be transmitted by the base station antenna in the simulation is increased from 10dBm to 40 dBm. According to simulation results, the secrecy capacity is increased along with the increase of the maximum power allowed to be transmitted by the base station antenna, and the secrecy capacity of the network resource configuration method based on the quantum bald eagle search mechanism is obviously superior to that of the network resource configuration method based on the bald eagle search mechanism.

Fig. 4 is a curve of the secret capacity of the intelligent reflector secure Massive MIMO network resource allocation method based on the quantum balde eagle search mechanism and the balde eagle search mechanism changing with the number of intelligent reflectors, and the number of reflectors in the simulation is increased from 10 to 100. According to simulation results, under the condition that other conditions are not changed, the number of the intelligent reflection elements is increased, and the secret capacity of the system is improved. Aiming at different intelligent reflection element numbers, the secrecy capacity of the network resource configuration method based on the quantum bald eagle search mechanism is always superior to that of the network resource configuration method based on the bald eagle search mechanism, and the effectiveness of the designed method is proved.