A method, system, and storage medium for weighted and rate optimization of wireless networks assisted by intelligent reflectors.

Abstract

This invention discloses a method, system, and storage medium for weighted and rate optimization of wireless networks assisted by intelligent reflectors, relating to the field of wireless communication, and addressing the problem of the lack of such optimization methods in existing technologies. The method includes: Step 1: Under transmit power constraints, establishing a weighted and rate optimization model for wireless networks assisted by intelligent reflectors based on the joint optimization of the transmit beamforming matrix at the base station and the diagonal matrix at the intelligent reflector; Step 2: Initializing a population of cultured Remora fish using the optimization model as the fitness function; Step 3: Allowing the cultured Remora fish to roam freely; Step 4: Allowing the cultured Remora fish to feed; Step 5: If the number of iterations is less than the preset maximum number of iterations, setting ε = ε + 1 and returning to Step 3; otherwise, terminating the iteration, outputting the global optimal position of the cultured Remora fish, returning the optimal fitness value, and finally obtaining the weighted and rate optimization method for wireless networks assisted by intelligent reflectors.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and more specifically, to a method, system, and storage medium for weighted and rate optimization of wireless networks assisted by intelligent reflectors. Background Technology

[0002] For sixth-generation and future mobile communication systems, multi-antenna systems are a key technology, promising to improve the performance of traditional communication systems by utilizing spatial degrees of freedom. However, obstacles still arise in multi-antenna systems due to the presence of buildings, trees, vehicles, and even humans. Smart reflectors, which can actively reconfigure the wireless propagation environment, offer a promising solution to this problem. Specifically, smart reflectors consist of a large number of intelligent, controllable, low-cost reflective elements that independently induce phase and amplitude changes in the incident signal. Smart reflectors can be viewed as a complement to existing wireless communication networks, and from an operational perspective, they can be integrated into buildings or existing wireless infrastructure.

[0003] Improving the power performance and spectral efficiency of received signals by jointly optimizing the transmit beamforming of the base station and the reflective beamforming of the intelligent reflector is the main research content of intelligent reflector-assisted communication systems. Considering different user fairness requirements, the formulation of next-generation wireless network optimization must be approached from the perspective of the entire system. In this paper, a multi-antenna base station provides communication services to multiple single-antenna mobile users, thus forming an intelligent reflector-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 reflector is deployed on the facades of surrounding buildings to help the base station overcome unfavorable propagation conditions. Through joint resource allocation of active beamforming at the base station and passive beamforming at the intelligent reflector, the goal of this paper is to maximize the weighted sum rate of mobile users. For intelligent reflector-assisted wireless communication scenarios, Yulan Gao et al., in their paper "Reconfigurable intelligentsurface for MISO systems with proportional rate constraints" published in the IEEE International Conference on Communications (2020: 1-7), explored the spectral efficiency optimization problem of intelligent reflector-assisted multi-input single-output multi-user downlink communication systems, but did not address the weighted sum rate optimization problem for mobile users. Yun Wu et al.'s paper "Average sum-rate maximization of coupled phase-shift STAR-RIS-assisted SWIPT-NOMA system" published in IEEE Communications Letters (2024) explores the average sum-rate optimization problem of a wireless power-carrying communication system with non-orthogonal multiple access assisted by a smart reflector, but does not consider the weighted sum-rate optimization problem.

[0004] Research on the weighted and rate optimization problem of wireless networks assisted by intelligent reflectors is still in its early stages. Currently, there is a lack of research on weighted and rate optimization methods for wireless networks assisted by intelligent reflectors. Summary of the Invention

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

[0006] Existing technologies lack methods for optimizing the weighted sum and rate of wireless networks with the assistance of intelligent reflectors.

[0007] The technical solution adopted by the present invention to solve the above-mentioned technical problems is as follows:

[0008] This invention provides a method for weighted and rate optimization of wireless networks with intelligent reflector-assisted calculation, characterized by the following steps:

[0009] Step 1: Under the constraint of transmit power, based on the joint optimization of the transmit beamforming matrix at the base station and the diagonal matrix at the smart reflector, establish a weighted and rate optimization model for wireless networks assisted by the smart reflector.

[0010] Step 2: Initialize the cultured Remora fish population using a smart reflector-assisted wireless network weighted and rate optimization model as the fitness function;

[0011] Step 3: The cultural Remora fish freely roams;

[0012] Step Four: Cultural Remora Fish Dining;

[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, outputs the global optimal position of the cultured Remora fish, returns the optimal fitness value, and thus obtains the weighted and rate optimization method for wireless networks assisted by intelligent reflectors.

[0014] Furthermore, step one includes the following steps:

[0015] The baseband equivalent channels from the base station to the smart reflector, from the base station to user k, and from the smart reflector to user k are respectively expressed as: and Where k = 1, 2, ..., K, M is the base station of the antenna, K is the single antenna user, and N is the number of reflection elements; a diagonal matrix Θ = diag(θ1, θ2, ..., θ) is used. N ) represents the phase shift matrix of the intelligent reflector, s k Let represent the data sent to user k, which is an independent random variable with zero mean and unit variance. The signal transmitted at the base station is represented as:

[0016]

[0017] in It is the corresponding transmitted beamforming vector;

[0018] The signal received at the k-th user receiver is represented as:

[0019]

[0020] in This represents additive Gaussian self-noise;

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

[0022]

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

[0024]

[0025] Where P T This represents the maximum transmit power at the base station;

[0026] Establish a weighted rate optimization model for wireless networks assisted by intelligent reflectors:

[0027] The optimization objective is set to maximize the weighted sum rate, specifically:

[0028]

[0029] Weighted sum rate

[0030] Where ω k The weight for user k;

[0031] The constraints are:

[0032]

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

[0034] A weighted and rate optimization model for wireless networks assisted by intelligent reflectors is used as the fitness function;

[0035] Initialize the population size F of the cultured Remora fish, the maximum number of iterations G, and the maximum dimension D of the search space; let... Let be the position of the i-th culture Remora fish in the ε-th generation, where d = 1, 2, ..., D, i = 1, 2, ..., F, and ε represents the current iteration number; let The optimal cultural position within the population;

[0036] First, randomly assign initial positions. in U is a random number uniformly distributed between 0 and 1. b and L b These are the largest and smallest positions of the cultural Remora fish.

[0037] Furthermore, step three, which describes the free navigation of the cultural Remora fish, 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, and model it as follows: in It is a random number generated between 0 and 1. This indicates a tentative operation;

[0039] Generate a random number between 0 and 1 if p1 is the threshold determined through sensitivity testing, and the updated independent variable is:

[0040]

[0041] The lower bound for updating the fitness value in generation ε is:

[0042]

[0043] if The updated independent variable is:

[0044]

[0045] The upper limit of the fitness value in generation ε is:

[0046]

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

[0048] if Update the location of the Remora fish in the Culture section:

[0049]

[0050] Where C is the Remora factor. and It is a random number generated between 0 and 1;

[0051] if Generate random numbers that satisfy a binomial distribution. if Set the value to 0, then update the location of the Remora fish in the Culture tree:

[0052]

[0053] in A random number generated between 0 and 1;

[0054] if Set the location of the Remora fish to 1.

[0055]

[0056] in Individuals randomly selected from the cultured Remora fish population.

[0057] The present invention also provides a smart reflector-assisted wireless network weighting and rate optimization system, which has a program module corresponding to the steps of any of the above-described technical solutions, and executes the steps in the smart reflector-assisted wireless network weighting and rate optimization method when running.

[0058] The present invention also provides a computer-readable storage medium storing a computer program configured to, when invoked by a processor, implement the steps of the intelligent reflector-assisted wireless network weighting and rate optimization method described in any of the above technical solutions.

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

[0060] This invention establishes a weighted sum rate optimization model for a smart reflector-assisted multi-input, single-output downlink communication system based on the joint optimization of the transmit beamforming matrix at the base station and the diagonal matrix at the smart reflector. This invention applies the Cultural Remora Fish algorithm to the weighted sum rate optimization problem to solve practical engineering issues, significantly improving the weighted sum rate of smart reflector-assisted wireless networks. The cultural mechanism of the proposed Cultural Remora Fish algorithm provides a framework for combining knowledge storage and evolutionary search mechanisms. The evolutionary search mechanism, based on population evolution, outperforms existing swarm intelligence algorithms in both convergence speed and accuracy.

[0061] The cultural Remora fish algorithm proposed in this invention has significant advantages in solving complex engineering application problems and high-dimensional optimization problems, and has broad application prospects in a wider range of engineering environments, providing a promising solution for solving optimization problems in engineering practice. Attached Figure Description

[0062] Figure 1 This is a schematic diagram of a wireless network weighting and rate optimization method based on a smart reflective surface assisted by a cultural Remora fish mechanism, as described in an embodiment of the present invention.

[0063] Figure 2 This is a graph showing the weighted sum rate of the wireless network assisted by the intelligent reflector based on the Cultural Remora Fish Algorithm (CRO) and the Remora Fish Algorithm (ROA) as a function of the number of iterations under the conditions of M=4 and N=20 in this embodiment of the invention.

[0064] Figure 3 This is a graph showing the weighted sum rate of the wireless network based on the Cultural Remora Fish algorithm and the Remora Fish algorithm with intelligent reflective surface assistance as a function of the number of iterations under the conditions of M=8 and N=40 in this embodiment of the invention.

[0065] Figure 4This is a graph showing the weighted sum rate of the wireless network based on the Remora fish algorithm and the intelligent reflector-assisted Remora fish algorithm as a function of the number of base station antennas in an embodiment of the present invention.

[0066] Figure 5 This is a graph showing the weighted sum rate of the wireless network assisted by the intelligent reflector based on the Remora fish algorithm and the Remora fish algorithm in an embodiment of the present invention, as a function of the number of intelligent reflector reflector units. Detailed Implementation

[0067] To enable those skilled in the art to better understand the present invention, 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 merely some, not all, of the embodiments or examples of the present invention. All other embodiments or examples obtained by those skilled in the art based on the embodiments or examples of the present invention without inventive effort should fall within the scope of protection of the present invention.

[0068] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0069] Specific Implementation Scheme 1: This invention provides a method for weighted and rate optimization of wireless networks assisted by intelligent reflectors, characterized by the following steps:

[0070] Step 1: Under the constraint of transmit power, based on the joint optimization of the transmit beamforming matrix at the base station and the diagonal matrix at the smart reflector, establish a weighted and rate optimization model for wireless networks assisted by the smart reflector.

[0071] Step 2: Initialize the cultured Remora fish population using a smart reflector-assisted wireless network weighted and rate optimization model as the fitness function;

[0072] Step 3: The cultural Remora fish freely roams;

[0073] Step Four: Cultural Remora Fish Dining;

[0074] 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, outputs the global optimal position of the cultured Remora fish, returns the optimal fitness value, and thus obtains the weighted and rate optimization method for wireless networks assisted by intelligent reflectors.

[0075] Specific Implementation Plan Two: Step One includes the following steps:

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

[0077]

[0078] in It is the corresponding transmitted beamforming vector;

[0079] The signal received at the k-th user receiver is represented as:

[0080]

[0081] in This represents additive Gaussian self-noise;

[0082] The interference-to-noise ratio of the decoded signal at user k is:

[0083]

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

[0085]

[0086] Where P T This represents the maximum transmit power at the base station;

[0087] The weighted sum rate optimization model for wireless networks assisted by intelligent reflectors is established as follows:

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

[0089]

[0090] Weighted sum rate

[0091] Where ωk The weight for user k;

[0092] The constraints are:

[0093]

[0094] This implementation plan is otherwise the same as Specific Implementation Plan 1.

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

[0096] A weighted and rate optimization model for wireless networks assisted by intelligent reflectors is used as the fitness function;

[0097] Initialize the population size F of the cultured Remora fish, the maximum number of iterations G, and the maximum dimension D of the search space; let... Let be the position of the i-th culture Remora fish in the ε-th generation, where d = 1, 2, ..., D, i = 1, 2, ..., F, and ε represents the current iteration number; let The optimal cultural position within the population;

[0098] First, randomly assign initial positions. in U is a random number uniformly distributed between 0 and 1. b and L b These are the maximum and minimum positions of the cultural Remora fish. This implementation plan is otherwise the same as specific implementation plan two.

[0099] Specific Implementation Plan Four: The free navigation 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, and model it as follows: in It is a random number generated between 0 and 1. This indicates a tentative operation;

[0101] Generate a random number between 0 and 1 if p1 is the threshold determined through sensitivity testing, and the updated independent variable is:

[0102]

[0103] The lower bound for updating the fitness value in generation ε is:

[0104]

[0105] if The updated independent variable is:

[0106]

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

[0108]

[0109] This implementation plan is otherwise the same as Implementation Plan 3.

[0110] Specific Implementation Plan Five: The cultural Remola fish dining described in Step Four includes the following steps:

[0111] if Update the location of the Remora fish in the Culture section:

[0112]

[0113] Where C is the Remora factor. and It is a random number generated between 0 and 1;

[0114] if Generate random numbers that satisfy a binomial distribution. if Set the value to 0, then update the location of the Remora fish in the Culture tree:

[0115]

[0116] in A random number generated between 0 and 1;

[0117] if Set the location of the Remora fish to 1.

[0118]

[0119] in Individuals were randomly selected from the cultured Remora fish population. This implementation plan is otherwise identical to Implementation Plan Four.

[0120] The intelligent reflector-assisted wireless network weighted rate optimization method (algorithm) proposed in this invention is the underlying technical core of this invention, and various products can be derived based on the algorithm.

[0121] Based on the method proposed in this invention, a smart reflector-assisted wireless network weighting and rate optimization system is developed using a programming language. This system has program modules corresponding to the steps of the above-described technical solution, and executes the steps in the smart reflector-assisted wireless network weighting and rate optimization method during runtime.

[0122] The developed system (software) computer program is stored on a computer-readable storage medium, and the computer program is configured to implement the steps of the above-described intelligent reflective surface-assisted wireless network weighting and rate optimization method when invoked by a processor. In other words, the invention is materialized on a carrier, becoming a computer program product.

[0123] Various implementations of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, application-specific integrated circuits (ASICs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0124] The computational programs (also referred to as programs, software, software applications, or code) of this invention include machine instructions of a programmable processor and 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., disk, optical disk, memory, programmable logic device PLD) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. 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 with reference to specific embodiments.

[0126] Example 1

[0127] This embodiment focuses on a smart reflector-assisted wireless network communication system. The parameter settings for the weighted sum rate optimization method for smart reflector-assisted wireless networks based on the cultured Remora fish mechanism are as follows: cultured Remora fish population size F = 50, auditory feature factor = 2, and selected random angles between 0 and 360 degrees. To facilitate comparison of the performance of the proposed cultured Remora fish mechanism, the traditional Remora fish algorithm is compared. [1] This method is applied to solve the weighted sum rate optimization problem of intelligent reflector-assisted wireless networks for comparison. The population size is set to the same value for both methods, the maximum number of iterations is 1000, and all results are the average of 100 simulation experiments. Figure 2 and Figure 3As shown in the simulation results, the weighted sum rate of the Cultural Remora Fish algorithm increases with the increase of the number of base station antennas and the number of smart reflector reflector units. Clearly, the Cultural Remora Fish algorithm of this invention significantly outperforms the Remora Fish algorithm in terms of convergence performance. Figure 4 As shown in the simulation, the number of base station antennas increased from 3 to 10. With the increase in the number of base station antennas, the weighted sum rate generally maintained an increasing trend, while the proposed Remora fish algorithm consistently maintained optimal performance. When the number of base station antennas is fixed, the weighted sum rate can be increased by increasing the number of reflective elements on the smart reflector. For example... Figure 5 As shown in the simulation, as the number of reflective elements increases from 10 to 80, the weighted sum rate maintains an increasing trend, and the proposed Remora fish algorithm consistently performs best. When the number of reflective elements on the smart reflector is fixed, the weighted sum rate can be increased by increasing the number of base station antennas.

[0128] While the present invention has been disclosed above, its scope of protection is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and all such changes and modifications will fall within the scope of protection of the present invention.

[0129] The documents cited in this invention include:

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

Claims

1. A method for weighted sum rate optimization of wireless networks assisted by intelligent reflectors, characterized in that, Includes the following steps: Step 1: Under the constraint of transmit power, based on the joint optimization of the transmit beamforming matrix at the base station and the diagonal matrix at the smart reflector, establish a weighted and rate optimization model for wireless networks assisted by the smart reflector. Step 2: Initialize the cultured Remora fish population using a smart reflector-assisted wireless network weighted and rate optimization model as the fitness function; Step 3: The cultural Remora fish freely roams; Step Four: Cultural Remora Fish Dining; Step 5: If the number of iterations is less than the preset maximum number of iterations, set the iteration count to zero. If the iteration terminates, return to step three; otherwise, output the global optimal position of the cultured Remora fish, return the optimal fitness value, and thus obtain the weighted and rate optimization method for wireless networks assisted by intelligent reflectors. Step one includes the following steps: The baseband equivalent channels from the base station to the smart reflector, from the base station to user k, and from the smart reflector to user k are respectively expressed as: , and Where k = 1, 2, ..., K, M is the base station with antennas, K is the number of users with a single antenna, and N is the number of reflecting elements; a diagonal matrix is ​​used. The phase shift matrix represents the intelligent reflector. Let represent the data sent to user k, which is an independent random variable with zero mean and unit variance. The signal transmitted at the base station is represented as: in It is the corresponding transmitted beamforming vector; The signal received at the k-th user receiver is represented as: in This represents additive white Gaussian noise; The interference-to-noise ratio of the decoded signal at user k is: make The transmit power constraint at the base station is: Where P T This represents the maximum transmit power at the base station; Establish a weighted rate optimization model for wireless networks assisted by intelligent reflectors: The optimization objective is set to maximize the weighted sum rate, specifically: Weighted sum rate ; in The weight for user k; The constraints are: ， Step two includes the following steps: A weighted and rate optimization model for wireless networks assisted by intelligent reflectors is used as the fitness function; Initialize the population size F of the cultured Remora fish, the maximum number of iterations G, and the maximum dimension D of the search space; let... For the first Let the position of the i-th culture Remora fish be given, where d = 1, 2, ..., D, i = 1, 2, ..., F. Let represent the current iteration number; The optimal cultural position within the population; First, randomly assign initial positions. ,in It is a random number uniformly distributed between 0 and 1. and These are the maximum and minimum positions of the cultural Remora fish; Step three describes the free navigation of the Remora fish, which 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, and model it as follows: ,in It is a random number generated between 0 and 1. , This indicates a tentative operation; Generate a random number between 0 and 1 ,if p1 is the threshold determined through sensitivity testing, and the updated independent variable is: Update fitness value The lower limit of the generation is: if The independent variable is updated as follows: Update fitness value The upper limit of generations is: Step four, the cultural Remora fish dining, includes the following steps: if Update the location of the Remora fish in the culture: Where C is the Remora factor. ,and It is a random number generated between 0 and 1; if Generate random numbers that satisfy a binomial distribution. ,if Set the value to 0, then update the location of the Remora fish in the Culture tree: in , , A random number generated between 0 and 1; if Set the location of the Remora fish to 1. in Individuals randomly selected from the cultured Remora fish population.

2. A smart reflector-assisted wireless network weighted and rate optimization system, characterized in that, The system has a program module corresponding to the steps of the method described in claim 1 above, and executes the steps in the above-described intelligent reflector-assisted wireless network weighting and rate optimization method when running.

3. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program configured to, when invoked by a processor, implement the steps of the intelligent reflector-assisted wireless network weighting and rate optimization method as described in claim 1.

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