Intelligent reflector-assisted wireless network weighting and rate optimization method and system, and storage medium

Abstract

The invention discloses a wireless network weighting and rate optimization method and system assisted by an intelligent reflecting surface, and a storage medium, relates to the field of wireless communication, and aims to solve the problem of lack of the wireless network weighting and rate optimization method assisted by the intelligent reflecting surface in the prior art. Comprising the following steps: step 1, under the constraint of transmitting power, based on joint optimization of a transmitting beam forming matrix at a base station and a diagonal matrix at an intelligent reflecting surface, establishing a wireless network weighting and rate optimization model assisted by the intelligent reflecting surface; step 2, taking the optimization model as a fitness function, and initializing a culture ramorala group; step 3, freely cruising the culture ramorala fish; step 4, feeding the culture ramorala fish; 5, if the number of iterations is smaller than the preset maximum number of iterations, enabling epsilon to be equal to epsilon + 1, and returning to the step 3; otherwise, iteration is terminated, the global optimal position of the culture ramorafish is output, the optimal fitness value is returned, and finally the wireless network weighting and rate optimization method assisted by the intelligent reflecting surface is obtained.

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 weighted sum rate of a wireless network assisted by an intelligent reflecting surface. Background Art

[0002] For the sixth-generation and future mobile communication systems, multi-antenna systems are a key technology, which is expected to improve the performance of traditional communication systems by exploiting spatial degrees of freedom. However, due to the presence of buildings, trees, cars, and even humans, obstacles still occur in multi-antenna systems. An intelligent reflecting surface can actively reconfigure the wireless propagation environment and is a promising method to solve this problem. Specifically, an intelligent reflecting surface consists of a large number of intelligent and controllable low-cost reflecting units, and the intelligent reflecting surface independently causes phase and amplitude changes of incident signals. The intelligent reflecting surface can be regarded as a supplement to the existing wireless communication network, and from an operational perspective, it can be integrated into buildings or existing wireless infrastructures.

[0003] Improving the power performance and spectral efficiency of received signals by jointly optimizing the transmit beamforming of the base station and the reflection beamforming of the intelligent reflecting surface is the main research content of the intelligent reflecting surface assisted communication system. Considering different user fairness requirements, the formulation of next-generation wireless network optimization must start from the perspective of the entire system. In this paper, a multi-antenna base station provides communication services for multiple single-antenna mobile users, thus forming an intelligent reflecting surface assisted multi-input single-output multi-user downlink communication system. By providing a high-quality virtual link from the base station to the users, the intelligent reflecting surface is deployed on the facade of surrounding buildings to help the base station overcome adverse propagation conditions. Through the joint resource allocation of active beamforming at the base station and passive beamforming at the intelligent reflecting surface, the goal of this paper is to maximize the weighted sum rate of mobile users. For the intelligent reflecting surface assisted wireless communication scenario, "Reconfigurable intelligent surface for MISO systems with proportional rate constraints" published by Yulan Gao et al. in 《IEEE International Conference on Communications》(2020: 1-7) discussed the spectral efficiency optimization problem of the intelligent reflecting surface assisted multi-input single-output multi-user downlink communication system, but did not involve the weighted sum rate optimization problem of mobile users. "Average sum-rate maximization of coupled phase-shift STAR-RIS-assisted SWIPT-NOMA system" published by Yun Wu et al. in 《IEEE Communications Letters》(2024) discussed the average sum rate optimization problem of the intelligent reflecting surface assisted wireless power transfer with non-orthogonal multiple access communication system, but did not consider the weighted sum rate optimization problem.

[0004] Currently, the research on the weighted sum rate optimization problem of intelligent reflecting surface assisted wireless networks is still in its infancy. At present, there is a lack of research on the weighted sum rate optimization method for intelligent reflecting surface assisted wireless networks. Summary of the Invention

[0005] The technical problem to be solved by the present invention is:

[0006] In the prior art, there is a lack of an optimization method for the weighted sum rate of intelligent reflecting surface assisted wireless networks.

[0007] The technical solution adopted by the present invention to solve the above technical problem:

[0008] The present invention provides a method for optimizing the weighted sum rate of a wireless network assisted by an intelligent reflecting surface, which is characterized by including the following steps:

[0009] Step 1: Under the transmit power constraint, based on the joint optimization of the transmit beamforming matrix at the base station and the diagonal matrix at the intelligent reflecting surface, establish an optimization model for the weighted sum rate of the wireless network assisted by the intelligent reflecting surface;

[0010] Step 2: Use the optimization model of the weighted sum rate of the wireless network assisted by the intelligent reflecting surface as the fitness function to initialize the cultural remora fish population;

[0011] Step 3: The cultural remora fish swim freely;

[0012] Step 4: The cultural remora fish eat;

[0013] Step 5: 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 position of the cultural remora fish, return the optimal fitness value, and further obtain the method for optimizing the weighted sum rate of the wireless network assisted by the intelligent reflecting surface.

[0014] Further, Step 1 includes the following steps:

[0015] Respectively represent the baseband equivalent channels from the base station to the intelligent reflecting surface, from the base station to user k, and from the intelligent reflecting surface to user k as and where k = 1, 2,..., K, M is the base station with antennas, K is the single-antenna user, and N is the number of reflection units; use the diagonal matrix Θ = diag(θ1, θ2,..., θ N ) to represent the phase shift matrix of the intelligent reflecting surface, s k represents the data sent to user k, which is an independent random variable with zero mean and unit variance, and the signal transmitted at the base station is expressed as:

[0016]

[0017] where is the corresponding transmit beamforming vector;

[0018] At the k-th user receiver, the received signal is expressed as:

[0019]

[0020] where represents the additive Gaussian white noise;

[0021] The signal-to-interference-plus-noise ratio of the decoded signal at user k is:

[0022]

[0023] Let The transmit power constraint at the base station is:

[0024]

[0025] where \(P\) T is the maximum transmit power at the base station;

[0026] Establish a weighted sum rate optimization model for the intelligent reflecting surface assisted wireless network:

[0027] Set the optimization objective to maximize the weighted sum rate, specifically:

[0028]

[0029] The weighted sum rate

[0030] where \(\omega\) k is the weight of user \(k\);

[0031] The constraint conditions are:

[0032]

[0033] Furthermore, step two includes the following steps:

[0034] Use the weighted sum rate optimization model of the intelligent reflecting surface assisted wireless network as the fitness function;

[0035] Initialize the number of cultural remora fish population \(F\), the maximum number of iterations \(G\), and the maximum dimension of the search space \(D\); Let be the position of the \(i\)-th cultural remora fish in the \(\epsilon\)-th generation, where \(d = 1, 2, \cdots, D\), \(i = 1, 2, \cdots, F\), and \(\epsilon\) represents the current iteration number; Let be the global best cultural position in the population;

[0036] First, randomly assign the initial position where is a random number uniformly distributed between 0 and 1, \(U\) b and \(L\) b are the maximum and minimum positions of the cultural remora fish.

[0037] Furthermore, the free cruising of the cultural remora fish in step three includes the following steps:

[0038] First, determine whether it is necessary to change the host. The cultural remora fish needs to take small steps of trial and error around the host, modeled as where is a random number generated between 0 and 1, Indicates a tentative operation;

[0039] Generate a random number between 0 and 1 If p1 is the threshold determined by the sensitivity test, and the independent variable is updated as:

[0040]

[0041] Update the lower limit of the fitness value for the ε-th generation as:

[0042]

[0043] If Update the independent variable as:

[0044]

[0045] Update the upper limit of the fitness value for the ε-th generation as:

[0046]

[0047] Furthermore, the cultural remora fish feeding described in step four includes the following steps:

[0048] If Update the position of the cultural remora fish:

[0049]

[0050] where C is the remora factor, And is a random number generated between 0 and 1;

[0051] If Generate a random number that satisfies the binomial distribution If is 0, update the position of the cultural remora fish:

[0052]

[0053] where is a random number generated between 0 and 1;

[0054] If is 1, update the position of the cultural remora fish:

[0055]

[0056] where is an individual randomly selected from the cultural remora fish population.

[0057] The present invention also provides an intelligent reflecting surface-assisted wireless network weighted sum rate 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 intelligent reflecting surface-assisted wireless network weighted sum rate optimization method described above when running.

[0058] 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-assisted wireless network weighted sum rate optimization method described in any one of the above technical solutions when called by a processor.

[0059] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0060] Based on the joint optimization of the transmit beamforming matrix at the base station and the diagonal matrix at the intelligent reflecting surface, the present invention establishes an intelligent reflecting surface-assisted multi-input and single-output downlink communication system weighted sum rate optimization model; the present invention applies the cultural remora fish algorithm to the weighted sum rate optimization problem to solve the problems in engineering practice, and greatly improves the weighted sum rate of the intelligent reflecting surface-assisted wireless network. The cultural mechanism of the cultural remora fish algorithm proposed by the present invention provides a framework for the combination of the knowledge storage and evolutionary search mechanisms. The evolutionary search mechanism is based on population evolution and is superior to the existing swarm intelligence algorithms in terms of convergence speed and convergence accuracy.

[0061] The cultural remora fish algorithm proposed by the present invention has significant advantages in solving complex engineering application problems and high-dimensional optimization problems, and has broad application prospects in a wider engineering environment, providing a promising solution for solving the optimization problems in engineering practice. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a schematic diagram of an intelligent reflecting surface-assisted wireless network weighted sum rate optimization method based on the cultural remora fish mechanism in an embodiment of the present invention;

[0063] Figure 2 It is a curve graph showing the change of the weighted sum rate of an intelligent reflecting surface-assisted wireless network with the number of iterations based on the cultural remora fish algorithm (CRO) and the remora fish algorithm (ROA) when M = 4 and N = 20 in an embodiment of the present invention;

[0064] Figure 3 It is a curve graph showing the change of the weighted sum rate of an intelligent reflecting surface-assisted wireless network with the number of iterations based on the cultural remora fish algorithm and the remora fish algorithm when M = 8 and N = 40 in an embodiment of the present invention;

[0065] Figure 4A graph showing the variation of the weighted sum rate of an intelligent reflecting surface-assisted wireless network based on the cultural remora fish algorithm and the remora fish algorithm with the number of base station antennas in an embodiment of the present invention;

[0066] Figure 5 A graph showing the variation of the weighted sum rate of an intelligent reflecting surface-assisted wireless network based on the cultural remora fish algorithm and the remora fish algorithm with the number of reflecting elements of the intelligent reflecting surface in an embodiment of the present invention. Detailed implementation manners

[0067] 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 protection scope of the present invention.

[0068] 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.

[0069] Specific implementation manner 1: The present invention provides an intelligent reflecting surface-assisted wireless network weighted sum rate optimization method, which is characterized by including the following steps:

[0070] Step 1: Under the transmit power constraint, based on the joint optimization of the transmit beamforming matrix at the base station and the diagonal matrix at the intelligent reflecting surface, an intelligent reflecting surface-assisted wireless network weighted sum rate optimization model is established;

[0071] Step 2: Using the intelligent reflecting surface-assisted wireless network weighted sum rate optimization model as the fitness function, initialize the cultural remora fish population;

[0072] Step 3: The cultural remora fish swim freely;

[0073] Step 4: The cultural remora fish eat;

[0074] Step 5: If the number of iterations is less than the pre-set maximum number of iterations, let ε = ε + 1, and return to Step 3; otherwise, the iteration terminates, output the global optimal position of the cultural remora fish, return the optimal fitness value, and thus obtain the intelligent reflecting surface-assisted wireless network weighted sum rate optimization method.

[0075] Specific implementation manner 2: Step 1 includes the following steps:

[0076] For an intelligent reflecting surface-assisted multi-input single-output multi-user downlink communication system consisting of a base station equipped with M antennas, K single-antenna users, and an intelligent reflecting surface with N reflecting elements; the baseband equivalent channels from the base station to the intelligent reflecting surface, from the base station to user k, and from the intelligent reflecting surface to user k are respectively denoted as and where k = 1, 2,..., K, M is the number of antennas of the base station, K is the number of single-antenna users, and N is the number of reflecting elements; the phase shift matrix of the intelligent reflecting surface is represented by the diagonal matrix Θ = diag(θ1, θ2,..., θ N ), s k represents the data transmitted to user k, which is an independent random variable with zero mean and unit variance, and the signal transmitted at the base station is represented as:

[0077]

[0078] where is the corresponding transmit beamforming vector;

[0079] At the k-th user receiver, the received signal is represented as:

[0080]

[0081] where represents the additive Gaussian self-noise;

[0082] The decoding signal-to-interference-plus-noise ratio at user k is:

[0083]

[0084] Let The transmit power constraint at the base station is:

[0085]

[0086] where P T is the maximum transmit power at the base station;

[0087] An intelligent reflecting surface-assisted wireless network weighted sum rate optimization model is established as:

[0088] Under the transmit power constraint, through the joint resource allocation of the transmit beamforming matrix W at the base station and the diagonal matrix Θ at the intelligent reflecting surface, the optimization objective is set to maximize the weighted sum rate, specifically:

[0089]

[0090] Weighted sum rate

[0091] where ωk The weight for user k;

[0092] The constraint conditions are:

[0093]

[0094] Other parts of this implementation scheme are the same as those of the first specific implementation scheme.

[0095] Specific implementation scheme three: Step two includes the following steps:

[0096] Use the intelligent reflecting surface-assisted wireless network weighted sum rate optimization model as the fitness function;

[0097] Initialize the number of cultural remora fish population F, the maximum number of iterations G, and the maximum dimension D of the search space; Let be the position of the i-th cultural remora fish in the ε-th generation, where d = 1, 2…, D, i = 1, 2…, F, and ε represents the current number of iterations; Let be the global best cultural position in the population;

[0098] First, randomly assign the initial position where is a random number uniformly distributed between 0 and 1, U b and L b are the maximum and minimum positions of the cultural remora fish. Other parts of this implementation scheme are the same as those of the second specific implementation scheme.

[0099] Specific implementation scheme four: The free cruising of the cultural remora fish described in step three includes the following steps:

[0100] First, determine whether it is necessary to change the host. The cultural remora fish needs to take small steps of trial and error around the host, modeled as where is a random number generated between 0 and 1, represents the tentative operation;

[0101] Generate a random number between 0 and 1 If p1 is the threshold determined through sensitivity tests, and update the independent variable as:

[0102]

[0103] Update the lower limit of the fitness value in the ε-th generation as:

[0104]

[0105] If Update the independent variable as:

[0106]

[0107] The upper limit of the fitness value updated in the ε-th generation is:

[0108]

[0109] Other parts of this implementation scheme are the same as those of the specific implementation scheme three.

[0110] Specific implementation scheme five: The steps of the cultural remora fish dining described in step four include the following steps:

[0111] If Update the position of the cultural remora fish:

[0112]

[0113] where C is the remora factor, And is a random number generated between 0 and 1;

[0114] If Generate a random number that satisfies the binomial distribution If is 0, update the position of the cultural remora fish:

[0115]

[0116] where is a random number generated between 0 and 1;

[0117] If is 1, update the position of the cultural remora fish:

[0118]

[0119] where is an individual randomly selected from the cultural remora fish population. Other parts of this implementation scheme are the same as those of the specific implementation scheme four.

[0120] An intelligent reflecting surface-assisted wireless network weighted sum rate optimization method (algorithm) proposed by the present invention is the underlying technical core of the present invention, and various products can be derived based on the algorithm.

[0121] Based on the method proposed by the present invention, an intelligent reflecting surface-assisted wireless network weighted sum rate optimization system 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 intelligent reflecting surface-assisted wireless network weighted sum rate optimization method when running.

[0122] Store the computer program of the developed system (software) on a computer-readable storage medium, and the computer program is configured to implement the steps of the above-mentioned intelligent reflecting surface-assisted wireless network weighted sum rate optimization method when called by a processor. That is, the present invention is materialized on a carrier to become a computer program product.

[0123] 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: 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, and can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0124] 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.

[0125] The beneficial effects of the present invention will be described below in conjunction with specific embodiments.

[0126] Embodiment 1

[0127] This embodiment is directed to an intelligent reflecting surface-assisted wireless network communication system. The parameter settings of the intelligent reflecting surface-assisted wireless network weighted sum rate optimization method based on the cultural remora fish mechanism are as follows: the cultural remora fish population size F = 50, the auditory feature factor is 2, and the selected random angle is between 0 and 360. To facilitate comparison of the performance of the proposed cultural remora fish mechanism, the traditional remora fish algorithm [1] is applied to solve the intelligent reflecting surface-assisted wireless network weighted sum rate 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. As Figure 2 and Figure 3As shown, it can be seen from the simulation results that the weighted sum rate of the cultural remora fish algorithm increases with the increase in the number of base station antennas and the number of reflecting elements of the intelligent reflecting surface. Obviously, the cultural remora fish algorithm of the present invention is significantly superior to the remora fish algorithm in terms of convergence performance. As Figure 4 shown, in the simulation, the number of base station antennas increases from 3 to 10. With the increase in the number of base station antennas, the weighted sum rate basically maintains an increasing trend, while the cultural remora fish algorithm of the present invention always maintains the best performance. When the number of base station antennas is fixed, the weighted sum rate can be increased by increasing the number of reflecting elements of the intelligent reflecting surface. As Figure 5 shown, in the simulation, the number of reflecting elements increases from 10 to 80. With the increase in the number of reflecting elements of the intelligent reflecting surface, the weighted sum rate maintains an increasing trend, and the cultural remora fish algorithm of the present invention always performs best. When the number of reflecting elements of the intelligent reflecting surface is fixed, the weighted sum rate can be increased by increasing the number of base station antennas.

[0128] 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.

[0129] The documents cited in the present invention include:

[0130] [1]Jia, Heming, et al. Remora optimization algorithm[J]. Expert Systems with Applications, 2021, v115665.

Claims

1. A method for optimizing the weighted sum rate of a wireless network assisted by an intelligent reflecting surface, characterized in that, It includes the following steps: Step 1: Under the transmit power constraint, based on the joint optimization of the transmit beamforming matrix at the base station and the diagonal matrix at the intelligent reflecting surface, establish a weighted sum-rate optimization model for the intelligent reflecting surface-assisted wireless network; Step 2: Use the weighted sum-rate optimization model of the intelligent reflecting surface-assisted wireless network as the fitness function to initialize the population of cultural remoras; Step 3: The cultural remoras swim freely; Step 4: The cultural remoras eat; Step 5: If the number of iterations is less than the pre-set maximum number of iterations, let ε = ε + 1, and return to Step 3; otherwise, the iteration terminates, output the global optimal position of the cultural remoras, return the optimal fitness value, and thus obtain the weighted sum-rate optimization method for the intelligent reflecting surface-assisted wireless network.

2. The method for optimizing the weighted sum rate of a wireless network assisted by an intelligent reflecting surface according to claim 1, wherein Step 1 includes the following steps: Denote the baseband equivalent channels from the base station to the intelligent reflecting surface, from the base station to user k, and from the intelligent reflecting surface to user k as and where k = 1, 2, …, K, M is the number of antennas of the base station, K is the number of single-antenna users, and N is the number of reflecting elements; use the diagonal matrix Θ = diag(θ1, θ2, ..., θ N ) to represent the phase shift matrix of the intelligent reflecting surface, s k represents the data transmitted to user k, which is an independent random variable with zero mean and unit variance, and the signal transmitted at the base station is expressed as: wherein is the corresponding transmit beamforming vector; At the k-th user receiver, the received signal is expressed as: wherein represents additive white Gaussian noise; The signal-to-interference-plus-noise ratio of the decoded signal at user k is: Let the transmit power constraint at the base station be: where P T is the maximum transmit power at the base station; Establish a weighted sum-rate optimization model for the intelligent reflecting surface-assisted wireless network: Set the optimization objective to maximize the weighted sum-rate, specifically: Weighted sum rate where ω k is the weight of user k; The constraint conditions are:

3. The intelligent reflecting surface-assisted wireless network weighted sum rate optimization method according to claim 2, wherein Step 2 includes the following steps: Use the weighted sum-rate optimization model of the intelligent reflecting surface-assisted wireless network as the fitness function; Initialize the population size F of cultural remora fish, the maximum number of iterations G, and the maximum dimension D of the search space; set as the position of the i-th cultural remora fish in the ε-th generation, where d = 1, 2…, D, i = 1, 2…, F, and ε represents the current number of iterations; set as the global best cultural position within the population; First, randomly assign an initial position where is a random number uniformly distributed between 0 and 1, U b and L b are the maximum and minimum positions of the cultural remora fish.

4. The intelligent reflecting surface-assisted wireless network weighted sum rate optimization method according to claim 3, wherein The free swimming of the cultural remoras in Step 3 includes the following steps: First, determine whether it is necessary to change the host. The cultural remora fish needs to take small steps of trial and error around the host, modeled as where is a random number generated between 0 and 1, represents a tentative operation; Generate a random number between 0 and 1 If p1 is the threshold determined by the sensitivity test, and the independent variable is updated as: Update the lower limit of the fitness value in the ε-th generation to: If The updated independent variable is: Update the upper limit of the fitness value in the ε-th generation to:

5. The intelligent reflecting surface-assisted wireless network weighted sum rate optimization method according to claim 4, wherein The eating of the cultural remoras in Step 4 includes the following steps: If Update the position of the cultural remora: where C is the remora factor, and is a random number generated between 0 and 1; If Generate random numbers that satisfy the binomial distribution If is 0, update the position of the cultural remora: wherein is a random number generated between 0 and 1; If is 1, update the position of the cultural remora: Among them are individuals randomly selected from the cultural remora fish population.

6. An intelligent reflecting surface-assisted wireless network weighted sum rate optimization system, characterized in that The system has program modules corresponding to the steps of the method described in any one of claims 1 to 5 above, and when running, executes the steps in the weighted sum-rate optimization method for the intelligent reflecting surface-assisted wireless network described above.

7. 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 weighted sum-rate optimization method for the intelligent reflecting surface-assisted wireless network described in any one of claims 1 to 5 when called by a processor.

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