Cognitive radio network security energy efficiency optimization method and system based on intelligent reflecting surface, and storage medium

Abstract

The invention discloses a cognitive radio network security energy efficiency optimization method and system based on an intelligent reflecting surface, and a storage medium, relates to the technical field of wireless communication, and aims to solve the problem of lack of the cognitive radio network security energy efficiency optimization method based on the intelligent reflecting surface in the prior art. Comprising the following steps: step 1, constructing a cognitive radio network security energy efficiency optimization model based on an intelligent reflecting surface based on transmitted beam forming of a cognitive radio base station and reflected beam forming of the intelligent reflecting surface; 2, taking the model as a fitness function, initializing a membrane quantum Archimedes algorithm, obtaining the position of a quantum object according to a mapping rule, and calculating the fitness; 3, updating the volume and density of the quantum object; 4, updating the acceleration of the quantum object; 5, evaluating the target fitness function, and searching a better quantum position for the quantum object; and step 6, repeatedly executing the step 3 to the step 5 until the maximum number of iterations is reached.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and in particular, to a method, system, and storage medium for optimizing the security energy efficiency of a cognitive radio network based on an intelligent reflecting surface. Background Art

[0002] Intelligent reflecting surfaces have recently attracted extensive attention from the wireless communication research community due to their ability to improve energy efficiency. An intelligent reflecting surface is an artificial surface made of electromagnetic materials, consisting of a large number of passive and low-cost reflecting elements that introduce phase shifts and amplitude changes to the incident signal. By doing so, the incident electromagnetic wave can be directed to the desired direction. Similar to a cooperative relay system, an intelligent reflecting surface can construct additional wireless links without the need for active radio frequency components. In addition, due to its passive structure, there is almost no additional power consumption and thermal noise during the reflection process. The intelligent reflecting surface is designed to assist wireless network transmissions without attempting to transmit information by itself.

[0003] Meanwhile, as an effective technology to enhance spectral efficiency, cognitive radio has been proposed. Specifically, cognitive radio networks based on spectrum sharing allow secondary users to share the spectrum with primary users while controlling the interference leakage to the primary user receivers. One design strategy is to maximize the achievable rate of secondary users while maintaining the interference temperature at the primary user receivers below a certain threshold. The threshold in the interference temperature constraint is obviously to ensure that the presence of secondary users does not cause an unacceptable degradation in the quality of service of primary users. In this case, beamforming technology is generally considered an effective means to support the optimal transmission scheme. For the communication scenario of intelligent reflecting surface-based cognitive radio networks, "Robust beamformer design in active RIS-assisted multiuser MIMO cognitive radio networks" published by Raviteja Allu et al. in 《IEEE Transactions on Cognitive Communications and Networking》(2023, vol.9, no.2, pp.398-413) proposed a method to solve the problem of jointly robust transmission, reflection, and reception strategy design in underlying multi-input multi-output intelligent reflecting surface networks with active intelligent reflecting surface assistance. "Joint sensing and transmission optimization for IRS-assisted cognitive radio networks" published by Wei Wu et al. in 《IEEE Transactions on Wireless Communications》(2023, vol.22, no.9, pp.5941-5956.) proposed a method to improve the accuracy of spectrum sensing and opportunistic spectrum access secondary transmission in intelligent reflecting surface-based cognitive radio networks. Currently, there is a lack of research on the optimization method of secure energy efficiency for intelligent reflecting surface-based cognitive radio networks. Summary of the Invention

[0004] The technical problem to be solved by the present invention is:

[0005] In the prior art, there is a lack of an optimization method for the secure energy efficiency of intelligent reflecting surface-based cognitive radio networks.

[0006] The technical solution adopted by the present invention to solve the above technical problem:

[0007] The present invention provides an optimization method for the secure energy efficiency of intelligent reflecting surface-based cognitive radio networks, including the following steps:

[0008] Step 1. Based on the transmit beamforming of the cognitive radio base station and the reflect beamforming of the intelligent reflecting surface, construct an optimization model for the secure energy efficiency of the cognitive radio network based on the intelligent reflecting surface;

[0009] Step 2. Take the optimization model for the secure energy efficiency of the cognitive radio network based on the intelligent reflecting surface as the fitness function, initialize the membrane quantum Archimedes algorithm, obtain the position of the quantum object according to the mapping rule, and calculate the fitness;

[0010] Step 3. Update the volume and density of the quantum object;

[0011] Step 4. Update the acceleration of the quantum object;

[0012] Step 5. Evaluate the target fitness function, and the quantum object searches for a better quantum position;

[0013] Step 6. If the number of iterations is less than the preset maximum number of iterations, let ε = ε + 1, and return to Step 3; otherwise, the iteration terminates, output the global optimal quantum position of the quantum object, obtain the position according to the mapping rule, return the optimal fitness value, and finally obtain the optimization method for the secure energy efficiency of the cognitive radio network based on the intelligent reflecting surface.

[0014] Furthermore, Step 1 includes the following process:

[0015] The channel coefficients from the cognitive radio base station to the intelligent reflecting surface, from the cognitive radio base station to the primary user, from the cognitive radio base station to the secondary user, from the cognitive radio base station to the eavesdropper, from the intelligent reflecting surface to the primary user, from the intelligent reflecting surface to the secondary user, and from the intelligent reflecting surface to the eavesdropper are respectively The signals received by the primary user, secondary user, and eavesdropper are uniformly constructed as:

[0016]

[0017] where x is the transmitted signal, w is the transmit beamforming of the cognitive radio base station, is the phase shift matrix of the intelligent reflecting surface, where diag(·) represents a diagonal matrix, δ l and χ l are respectively the phase shift and amplitude reflection coefficient of the l-th reflecting element. Assume n v is additive complex Gaussian white noise with zero mean and variance of

[0018] Let Construct the signals received by the primary user, secondary user, and eavesdropper as:

[0019] y v = qH H v wx + n v

[0020] where the reflected beamforming of the intelligent reflecting surface

[0021] The signal-to-noise ratio at the receiver is Assume that the eavesdropper intercepts the signal sent by the cognitive radio base station. The achievable secrecy rate at the secondary user is:

[0022]

[0023] The energy consumed by the cognitive radio base station includes the transmit power ||w|| 2 and the circuit power P CBS , and the power consumed by the intelligent reflecting surface is P IRS . Therefore, the total power consumption of the considered network is:

[0024] P t = ζ||w|| 2 + P CBS + P IRS

[0025] where ζ is the amplifier coefficient and ||.|| is the Euclidean norm;

[0026] Adopt the secure energy efficiency as the performance metric to calculate the secret bits per unit energy and bandwidth during the transmission, i.e.:

[0027]

[0028] Furthermore, the secure energy efficiency is:

[0029]

[0030] Construct an optimization model for the secure energy efficiency of a cognitive radio network based on an intelligent reflecting surface:

[0031] Set the optimization objective to maximize the secure energy efficiency, specifically:

[0032]

[0033] The constraints are:

[0034]

[0035] where represents the maximum transmit power of the cognitive radio base station, represents the minimum acceptable secrecy rate threshold.

[0036] Furthermore, step two includes the following process:

[0037] Initialize the number K of quantum objects, the maximum number of iterations E, and the search space dimension D; Let the quantum position of the k-th quantum object be where Obtain the position of the k-th quantum object according to the mapping rule The specific mapping rule is: where U d and L d are the upper and lower bounds of the d-th search interval of the quantum object; Use the cognitive radio network security energy efficiency optimization model based on intelligent reflecting surface as the fitness function Calculate the fitness of the position of the k-th quantum object in the ε-th generation.

[0038] Furthermore, step three includes the following process:

[0039] The volume and density of the k-th quantum object are respectively and Update the k-th

[0040] The volume of the (ε + 1)-th generation of the k-th quantum object is:

[0041]

[0042] Update the density of the (ε + 1)-th generation of the k-th quantum object to:

[0043]

[0044] where and are random numbers uniformly distributed in the domain [0, 1];

[0045] The volume and density of the global optimal quantum object are and

[0046] Furthermore, step four includes the following process:

[0047] The acceleration of the k-th quantum object is Calculate the transfer operator:

[0048]

[0049] Generate a random number r in the domain [0, 1] b , if r b > p1, where p1 is a threshold determined by sensitivity test, update the d-th acceleration of the k-th quantum object to:

[0050]

[0051] where r is a single label randomly selected from the group of quantum objects, is the acceleration of the globally optimal quantum object, is the probability of quantum object transfer;

[0052] The acceleration of the normalized quantum object is:

[0053]

[0054] where u d and l d are the normalization ranges in the d-th dimension, and are the maximum and minimum values of the acceleration;

[0055] If r b ≤ p1, update the acceleration of the k-th quantum object in the d-th dimension as:

[0056]

[0057] Furthermore, step five includes the following process:

[0058] Calculate the density factor:

[0059]

[0060] When r b > p1, update the quantum rotation angle of the k-th quantum object in the d-th dimension as:

[0061]

[0062] Update the quantum position of the k-th quantum object in the d-th dimension as:

[0063]

[0064] where is the best global quantum position, and are both uniformly random numbers in the domain [0, 1], and S represents changing the motion direction of the quantum object:

[0065]

[0066] where P ∈ [-ω4, 2 - ω4], is the probability of the motion direction of the quantum object, and ω1, ω2, ω3, and ω4 are learning factors;

[0067] When r b ≤ p1, generate a random number in the domain [0, 1] Update the quantum rotation angle of the k-th quantum object in the d-th dimension as:

[0068]

[0069] wherein and are both random numbers uniformly distributed in the domain [0, 1], is a random number subject to the standard Gaussian distribution, p2 is the threshold determined by the sensitivity test, ν ε is the weight inertia coefficient, and the calculation formula is:

[0070]

[0071] where v max and v min are the upper and lower limits of ν ε ;

[0072] Update the d - dimensional quantum position of the k - th quantum object:

[0073]

[0074] The present invention also provides an intelligent reflecting surface - based cognitive radio network security energy efficiency optimization system, which has program modules corresponding to the steps of the method described in any one of the above technical solutions, and executes the steps in the above - mentioned intelligent reflecting surface - based cognitive radio network security energy efficiency optimization method when running.

[0075] The present invention also provides a computer - readable storage medium, which stores a computer program configured to implement the steps in the intelligent reflecting surface - based cognitive radio network security energy efficiency optimization method described in any one of the above technical solutions when called by a processor.

[0076] Compared with the prior art, the beneficial effects of the present invention are:

[0077] An intelligent reflecting surface - based cognitive radio network security energy efficiency optimization method of the present invention, in order to achieve the trade - off between the secrecy rate and energy consumption, studies the problem of maximizing the security energy efficiency by jointly designing the transmit beamforming of the cognitive radio base station and the reflection beamforming of the intelligent reflecting surface, and constructs an intelligent reflecting surface - based cognitive radio network security energy efficiency optimization model; and proposes a brand - new membrane quantum Archimedes algorithm to solve the optimization problem of the security energy efficiency in the intelligent reflecting surface - assisted cognitive radio network, greatly improving the security energy efficiency of the intelligent reflecting surface - based cognitive radio network, and providing a preliminary attempt to solve the problems in engineering practice. The membrane quantum Archimedes algorithm of the present invention performs excellently in terms of search speed and search accuracy; it has broad application prospects in a wider engineering application environment and has advantages in solving complex engineering application problems and high - dimensional optimization problems.

[0078] Starting from quantum computing and membrane structures, the present invention proposes a brand-new membrane quantum Archimedes algorithm to obtain the optimal solution of the objective function, further improving both the search accuracy and search speed, and enabling better convergence performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 It is a schematic diagram of the intelligent reflecting surface cognitive radio network security energy efficiency optimization method based on the membrane quantum Archimedes mechanism in the embodiment of the present invention;

[0080] Figure 2 It is a graph showing the change of the security energy efficiency of the intelligent reflecting surface cognitive radio network security energy efficiency optimization method based on the membrane quantum Archimedes algorithm (MQAOA) and the Archimedes algorithm (AOA) with the number of iterations when N = 4 and L = 30 in the embodiment of the present invention;

[0081] Figure 3 It is a graph showing the change of the security energy efficiency of the intelligent reflecting surface cognitive radio network security energy efficiency optimization method based on the membrane quantum Archimedes algorithm and the Archimedes algorithm with the number of iterations when N = 8 and L = 60 in the embodiment of the present invention;

[0082] Figure 4 It is a graph showing the change of the security energy efficiency of the intelligent reflecting surface cognitive radio network security energy efficiency optimization method based on the membrane quantum Archimedes algorithm and the Archimedes algorithm with the number of antennas of the cognitive radio base station;

[0083] Figure 5 It is a graph showing the change of the security energy efficiency of the intelligent reflecting surface cognitive radio network security energy efficiency optimization method based on the membrane quantum Archimedes algorithm and the Archimedes algorithm with the number of reflecting elements of the intelligent reflecting surface. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0084] In order to enable those skilled in the art to better understand the solution of the present invention, the exemplary embodiments or examples of the present invention will be described below in conjunction with the accompanying drawings. Obviously, the described embodiments or examples are only a part of the embodiments or examples of the present invention, rather than all of them. All other embodiments or examples obtained by those of ordinary skill in the art based on the embodiments or examples of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0085] To make the above objects, features, and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0086] Specific implementation plan 1: The present invention provides a method for optimizing the security energy efficiency of a cognitive radio network based on an intelligent reflecting surface, including the following steps:

[0087] Step 1: Based on the transmit beamforming of the cognitive radio base station and the reflect beamforming of the intelligent reflecting surface, construct an optimization model for the security energy efficiency of the cognitive radio network based on the intelligent reflecting surface;

[0088] Step 2: Use the optimization model of the security energy efficiency of the cognitive radio network based on the intelligent reflecting surface as the fitness function, initialize the membrane quantum Archimedes algorithm, obtain the position of the quantum object according to the mapping rule, and calculate the fitness;

[0089] Step 3: Update the volume and density of the quantum object;

[0090] Step 4: Update the acceleration of the quantum object;

[0091] Step 5: Evaluate the target fitness function, and the quantum object searches for a better quantum position;

[0092] Step 6: If the number of iterations is less than the preset maximum number of iterations, let ε = ε + 1, and return to Step 3; otherwise, the iteration terminates, output the global optimal quantum position of the quantum object, obtain the position according to the mapping rule, return the optimal fitness value, and finally obtain the method for optimizing the security energy efficiency of the cognitive radio network based on the intelligent reflecting surface.

[0093] The invention studies the application of an intelligent reflecting surface in an underlying cognitive radio network, where a multi-antenna cognitive radio base station communicates with a secondary user through the intelligent reflecting surface using the spectrum allocated to the primary user in the presence of an eavesdropper. To achieve a trade-off between the secrecy rate and energy consumption, the problem of maximizing the security energy efficiency is studied by jointly designing the transmit beamforming of the cognitive radio base station and the reflect beamforming of the intelligent reflecting surface.

[0094] Specific implementation plan 2: Step 1 includes the following process:

[0095] Consider an intelligent reflecting surface-assisted cognitive radio network: The cognitive radio base station communicates with the secondary user through the intelligent reflecting surface using the spectrum allocated to the primary user, where the eavesdropper attempts to intercept the information transmission between the cognitive radio base station and the secondary user. Assume that the cognitive radio base station is equipped with N antennas, and the primary user, secondary user, and eavesdropper have single antennas. To enhance the energy efficiency of the cognitive radio base station, an intelligent reflecting surface is deployed on the facade of a high-rise building. The intelligent reflecting surface consists of L passive reflecting elements, and each element can flexibly adjust the phase of the incident electromagnetic wave.

[0096] The channel coefficients from the cognitive radio base station to the intelligent reflecting surface, from the cognitive radio base station to the primary user, from the cognitive radio base station to the secondary user, from the cognitive radio base station to the eavesdropper, from the intelligent reflecting surface to the primary user, from the intelligent reflecting surface to the secondary user, and from the intelligent reflecting surface to the eavesdropper are respectively When the cognitive radio base station sends signals to the secondary user through the intelligent reflecting surface, due to the long distance between the primary base station and the cognitive radio base station, the interference from the primary base station to the secondary user and the eavesdropper can be regarded as noise. The signals received by the primary user, secondary user, and eavesdropper are uniformly constructed as follows:

[0097]

[0098] where \(x\) is the transmitted signal, \(w\) is the transmit beamforming at the cognitive radio base station, is the phase shift matrix of the intelligent reflecting surface, where \(diag(·)\) represents a diagonal matrix, \(\delta\) l and \(\chi\) l are the phase shift and amplitude reflection coefficient of the \(i\)-th reflecting element respectively. In practice, it is very costly to achieve independent control of both the reflection amplitude and phase. Therefore, for simplicity, each element is usually designed to maximize signal reflection. Thus, this implementation assumes n v is additive complex Gaussian white noise with zero mean and variance

[0099] Let The signals received by the primary user, secondary user, and eavesdropper are constructed as follows:

[0100] y v =q H H v wx + n v

[0101] where the reflection beamforming of the intelligent reflecting surface

[0102] The signal-to-noise ratio at the receiving end is Assume that the eavesdropper eavesdrops on the signals sent by the cognitive radio base station. The achievable secrecy rate at the secondary user is:

[0103]

[0104] The energy consumed by the cognitive radio base station includes the transmit power \(\vert\vert w\vert\vert\) 2 and the circuit power \(P\) CBS , and the power consumed by the intelligent reflecting surface is \(P\) IRS . Therefore, the total power consumption of the considered network is:

[0105] Pt = ζ||w|| 2 + P CBS + P IRS

[0106] where ζ is the amplifier coefficient and ||·|| is the Euler norm;

[0107] To maintain a balance between the secrecy rate and the total power consumed by the system, the secure energy efficiency is adopted as a performance metric to calculate the secret bits per unit energy and bandwidth during the transmission, i.e.:

[0108]

[0109] The objective of the present invention is to solve the optimization problem of secure energy efficiency on the premise of ensuring the secure transmission requirements of the secondary users and the transmit power of cognitive transmission. Further, the secure energy efficiency is:

[0110]

[0111] For the cognitive radio network containing intelligent reflecting surfaces, a secure energy efficiency optimization model of the cognitive radio network based on intelligent reflecting surfaces is constructed:

[0112] The optimization objective is set to maximize the secure energy efficiency, specifically:

[0113]

[0114] The constraint conditions are:

[0115]

[0116] where represents the maximum transmit power of the cognitive radio base station, represents the minimum acceptable secrecy rate threshold. It aims to ensure that the secure transmission of the secondary users reaches a certain level according to the user requirements. Other parts of this implementation scheme are the same as those of the first specific implementation scheme.

[0117] Specific implementation scheme three: Step two includes the following process:

[0118] Initialize the number of quantum objects K, the maximum number of iterations E, and the search space dimension D; Let the quantum position of the k-th quantum object be where The position of the k-th quantum object is obtained according to the mapping rule The specific mapping rule is: where U d and L dare the upper and lower bounds of the search interval for the d-th dimension of the quantum object; the quantum position of each quantum object corresponds to an optimization scheme for the secure energy efficiency of the intelligent reflecting surface-based cognitive radio network. Calculate the fitness of the position of the k-th quantum object in the ε-th generation through the fitness function of the secure energy efficiency optimization problem of the intelligent reflecting surface-based cognitive radio network. After generating the initial quantum object positions, use the secure energy efficiency optimization model of the intelligent reflecting surface-based cognitive radio network as the fitness function Calculate the fitness of the position of the k-th quantum object in the ε-th generation. Other aspects of this implementation are the same as those of the second specific implementation

[0119] Specific implementation four: Step three includes the following process

[0120] The volume and density of the k-th quantum object are respectively and Update the volume of the k-th quantum object in the (ε + 1)-th generation to

[0121]

[0122] Update the density of the k-th quantum object in the (ε + 1)-th generation to

[0123]

[0124] where and are random numbers uniformly distributed in the domain [0, 1];

[0125] The volume and density of the global optimal quantum object are and Other aspects of this implementation are the same as those of the third specific implementation

[0126] Specific implementation five: Step four includes the following process

[0127] The acceleration of the k-th quantum object is Calculate the transfer operator

[0128]

[0129] Generate a random number r in the domain [0, 1] b , if r b > p1, where p1 is a threshold determined through sensitivity tests, update the d-th dimension acceleration of the k-th quantum object to

[0130]

[0131] where r is a single label randomly selected from the quantum object group is the acceleration of the global optimal quantum object is the probability of quantum object transfer;

[0132] The acceleration of the normalized quantum object is:

[0133]

[0134] where u d and l d are the normalized ranges in the d-th dimension, and are the maximum and minimum values of the acceleration;

[0135] If r b ≤ p1, update the acceleration in the d-th dimension of the k-th quantum object as:

[0136]

[0137] The other parts of this implementation scheme are the same as those of the fourth specific implementation scheme.

[0138] Specific implementation scheme six: Step five includes the following process:

[0139] Calculate the fitness, and evaluate the new quantum position of the quantum object by the fitness of the cognitive radio network security energy efficiency optimization problem based on the intelligent reflecting surface. The quantum position of each quantum object needs to be updated.

[0140] Calculate the density factor:

[0141]

[0142] When r b > p1, update the quantum rotation angle in the d-th dimension of the k-th quantum object as:

[0143]

[0144] Update the quantum position in the d-th dimension of the k-th quantum object as:

[0145]

[0146] where is the best global quantum position, and are both uniformly random numbers in the domain [0, 1], and S represents changing the movement direction of the quantum object:

[0147]

[0148] where P ∈ [-ω4, 2 - ω4], is the probability of the movement direction of the quantum object, and ω1, ω2, ω3, and ω4 are learning factors;

[0149] When r b ≤ p1, generate a random number in the domain [0, 1] The quantum rotation angle of the d-th dimension of the k-th quantum object is updated as follows:

[0150]

[0151] where and are both random numbers uniformly distributed in the domain [0, 1], is a random number obeying the standard Gaussian distribution, p2 is a threshold determined by the sensitivity test, and ν ε is the weight inertia coefficient, and its calculation formula is:

[0152]

[0153] where v max and v min are the upper and lower limits of ν ε ;

[0154] Update the quantum position of the d-th dimension of the k-th quantum object:

[0155]

[0156] Other parts of this implementation scheme are the same as those of the fifth specific implementation scheme.

[0157] A method (algorithm) for optimizing the security energy efficiency of a cognitive radio network based on an intelligent reflecting surface proposed by the present invention is the underlying technical core of the present invention. Various products can be derived based on the said algorithm.

[0158] Based on the method proposed by the present invention, a system for optimizing the security energy efficiency of a cognitive radio network based on an intelligent reflecting surface is developed using a programming language. The system has program modules corresponding to the steps of the above technical solution, and executes the steps in the above method for optimizing the security energy efficiency of a cognitive radio network based on an intelligent reflecting surface when running.

[0159] The computer program of the developed system (software) is stored on a computer-readable storage medium. The computer program is configured to implement the steps of the above method for optimizing the security energy efficiency of a cognitive radio network based on an intelligent reflecting surface when called by a processor. That is, the present invention is materialized on a carrier and becomes a computer program product.

[0160] The various embodiments of the systems and techniques described herein can be implemented in digital electronic circuitry, integrated circuit systems, dedicated ASICs (application specific integrated circuits), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special or general programmable processor that receives data and instructions from a storage system, at least one input device, and at least one output device, and transmits the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0161] The computing programs (also referred to as programs, software, software applications, or code) in the present invention include machine instructions for a programmable processor, and these computing programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., magnetic disks, optical disks, memories, programmable logic device PLD) for providing machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0162] The beneficial effects of the present invention will be described below in conjunction with specific embodiments.

[0163] Embodiment 1

[0164] For the intelligent reflecting surface-based cognitive radio network system, the parameter settings of the intelligent reflecting surface cognitive radio network security energy efficiency optimization method based on the membrane quantum Archimedes mechanism are as follows: the membrane quantum Archimedes population size K = 50, u = 0.9, l = 0.1, p1 = 0.8, p2 = 0.9, ω1 = 2, ω2 = 6, ω3 = 2, ω4 = 0.5, and the initial quantum positions of the membrane quantum Archimedes are randomly generated within the quantum position domain. To facilitate the comparison of the performance of the membrane quantum Archimedes mechanism proposed in the present invention, the traditional Archimedes algorithm [1] is applied to solve the intelligent reflecting surface cognitive radio network security energy efficiency optimization problem as a comparison, and the population sizes of the two are set to the same value, and the maximum number of iterations is 1000 times for both. All results are the means of 100 simulation experiments.

[0165] From Figure 2 and Figure 3From the simulation results, it can be seen that the secure energy efficiency of the membrane quantum Archimedes algorithm of the present invention increases with the increase in the number of antennas of the cognitive radio base station and the number of reflecting elements of the intelligent reflecting surface, and is significantly superior to the traditional Archimedes algorithm in terms of convergence performance.

[0166] As Figure 4 shown, it is the curve of the secure energy efficiency of the secure energy efficiency optimization method for the intelligent reflecting surface cognitive radio network based on the membrane quantum Archimedes algorithm and the Archimedes algorithm changing with the number of antennas of the cognitive radio base station. In the simulation, the number of base station antennas increases from 3 to 10. It can be seen from the simulation test that with the increase in the number of base station antennas, the secure energy efficiency basically shows a downward trend, and the membrane quantum Archimedes algorithm of the present invention always maintains the best performance.

[0167] As Figure 5 shown, it is the curve of the secure energy efficiency of the secure energy efficiency optimization method for the intelligent reflecting surface cognitive radio network based on the membrane quantum Archimedes algorithm and the Archimedes algorithm changing with the number of reflecting elements of the intelligent reflecting surface. In the simulation, the number of reflecting elements of the intelligent reflecting surface increases from 10 to 80. It can be seen from the simulation test that with the increase in the number of reflecting elements of the intelligent reflecting surface, the secure energy efficiency basically remains unchanged, and the membrane quantum Archimedes algorithm of the present invention always maintains the best performance.

[0168] Although the present invention is disclosed as above, the protection scope of the present invention is not limited thereto. Those skilled in the art of the present invention can make various changes and modifications without departing from the spirit and scope of the present disclosure, and these changes and modifications will all fall within the protection scope of the present invention.

[0169] The documents cited in the present invention include:

[0170] [1]Hashim, Fatma A, et al. Archimedes optimization algorithm: a newmetaheuristic algorithm for solving optimization problems[J]. AppliedIntelligence, 2021, vol.51, pp.1531-1551.

Claims

1. A method for optimizing the security energy efficiency of a cognitive radio network based on intelligent reflecting surfaces, characterized in that It includes the following steps: Step 1: Based on the transmit beamforming of the cognitive radio base station and the reflect beamforming of the intelligent reflecting surface, construct an optimization model for the secure energy efficiency of the cognitive radio network based on the intelligent reflecting surface; Step 2: Use the optimization model for the secure energy efficiency of the cognitive radio network based on the intelligent reflecting surface as the fitness function, initialize the membrane quantum Archimedes algorithm, obtain the position of the quantum object according to the mapping rule and calculate the fitness; Step 3: Update the volume and density of the quantum object; Step 4: Update the acceleration of the quantum object; Step 5: Evaluate the target fitness function, and the quantum object searches for a better quantum position; Step 6: If the number of iterations is less than the preset maximum number of iterations, let ε = ε + 1, and return to Step 3; otherwise, the iteration terminates, output the global optimal quantum position of the quantum object, obtain the position according to the mapping rule, return the optimal fitness value, and finally obtain the optimization method for the secure energy efficiency of the cognitive radio network based on the intelligent reflecting surface.

2. The method for optimizing the security energy efficiency of a cognitive radio network based on an intelligent reflecting surface according to claim 1, wherein Step 1 includes the following process: The channel coefficients from the cognitive radio base station to the intelligent reflecting surface, from the cognitive radio base station to the primary user, from the cognitive radio base station to the secondary user, from the cognitive radio base station to the eavesdropper, from the intelligent reflecting surface to the primary user, from the intelligent reflecting surface to the secondary user, and from the intelligent reflecting surface to the eavesdropper are respectively The signals received by the primary user, secondary user, and eavesdropper are uniformly constructed as: where \(x\) is the transmitted signal and \(w\) is the transmit beamforming of the cognitive radio base station, is the phase shift matrix of the intelligent reflecting surface, where \(diag(·)\) represents the diagonal matrix, \(\delta\) l and \(\chi\) l are the phase shift and amplitude reflection coefficient of the \(l\)-th reflecting element, respectively. Assume that \(n\) v is additive complex Gaussian white noise with zero mean and variance of Let Construct the signals received by the primary user, secondary user, and eavesdropper as follows: y v = q H H v wx + n v Among them, the reflection beamforming of the intelligent reflecting surface The signal-to-noise ratio at the receiver is Assume that an eavesdropper intercepts the signal sent by the cognitive radio base station. The achievable secrecy rate at the secondary user is as follows: The energy consumed by the cognitive radio base station includes the transmit power ||w|| 2 and the circuit power P CBS , and the power consumed by the intelligent reflecting surface is P IRS , therefore, the total power consumption of the considered network is: P t = ζ||w|| 2 + P CBS + P IRS where ζ is the amplifier coefficient and ||.|| is the Euler norm; Use the secure energy efficiency as a performance metric to calculate the secret bits per unit energy and bandwidth during transmission, that is: Furthermore, the secure energy efficiency is: Construct an optimization model for the secure energy efficiency of the cognitive radio network based on the intelligent reflecting surface: Set the optimization objective to maximize the secure energy efficiency, specifically: The constraint conditions are: Among them represents the maximum transmission power of the cognitive radio base station, represents the minimum acceptable secrecy rate threshold.

3. The method for optimizing the security energy efficiency of a cognitive radio network based on an intelligent reflecting surface according to claim 2, characterized in that Step 2 includes the following process: Initialize the number K of quantum objects, the maximum number of iterations E, and the search space dimension D; let the quantum position of the k-th quantum object be where Obtain the position of the k-th quantum object according to the mapping rule The specific mapping rule is: where U d and L d are the upper and lower bounds of the d-th search interval of the quantum object; use the security energy efficiency optimization model of the intelligent reflecting surface-based cognitive radio network as the fitness function Calculate the fitness of the position of the k-th quantum object in the ε-th generation.

4. The method for optimizing the security energy efficiency of a cognitive radio network based on an intelligent reflecting surface according to claim 3, characterized in that Step 3 includes the following process: The volume and density of the k-th quantum object are respectively and Update the volume of the (ε + 1)-th generation of the k-th quantum object to be: Update the density of the k-th quantum object in the (ε + 1)-th generation as: where and are random numbers uniformly distributed in the domain [0, 1]; The volume and density of the globally optimal quantum object are and 5. The method for optimizing the security energy efficiency of a cognitive radio network based on an intelligent reflecting surface according to claim 4, wherein Step 4 includes the following process: The acceleration of the k-th quantum object is Calculate the transfer operator: Generate a random number r in the domain [0, 1] b , if r b > p1, where p1 is the threshold determined by the sensitivity test, update the acceleration of the d-th dimension of the k-th quantum object as: where f is a single label randomly selected from the group of quantum objects, is the acceleration of the globally optimal quantum object, is the probability of quantum object transfer; Normalize the acceleration of the quantum object as: where u d and l d are the normalized ranges in the d-th dimension, and are the maximum and minimum values of the acceleration; If r b ≤ p1, update the acceleration of the k-th quantum object in the d-th dimension as follows:

6. The method for optimizing the security energy efficiency of a cognitive radio network based on an intelligent reflecting surface according to claim 5, wherein Step 5 includes the following process: Calculate the density factor: When r b > p1, the quantum rotation angle of the d-th dimension of the k-th quantum object is updated as follows: Update the d-th dimensional quantum position of the k-th quantum object as: wherein is the optimal global quantum position, and are both uniformly random numbers in the domain [0, 1], and S represents changing the motion direction of the quantum object: where \(P\in[-\omega_4, 2-\omega_4]\), is the probability of the moving direction of the quantum object, and \(\omega_1\), \(\omega_2\), \(\omega_3\) and \(\omega_4\) are learning factors; When r b ≤ p1, generate a random number in the domain [0, 1] The quantum rotation angle of the d-th dimension of the k-th quantum object is updated to: wherein and are both random numbers subject to a uniform distribution in the domain [0, 1], is a random number subject to a standard Gaussian distribution, p2 is a threshold determined through a sensitivity test, and ν ε is a weight inertia coefficient, and the calculation formula is: where v max and v min are the upper and lower limits of ν ε ; Update the d-th dimensional quantum position of the k-th quantum object:

7. A cognitive radio network security energy efficiency optimization system based on intelligent reflecting surface, characterized in that The system has program modules corresponding to the steps of the method described in any one of claims 1 to 6 above, and when running, executes the steps in the above-mentioned optimization method for the secure energy efficiency of the cognitive radio network based on the intelligent reflecting surface.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and the computer program is configured to implement the steps in the optimization method for the secure energy efficiency of the cognitive radio network based on the intelligent reflecting surface described in any one of claims 1 to 6 when called by a processor.

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