Electromagnetic wave propagation path optimization system based on intelligent metasurface

Abstract

The invention discloses an electromagnetic wave propagation path optimization system based on an intelligent metasurface, and particularly relates to the technical field of wireless communication. The optimization algorithm is efficient in adaptation, multiple requirements are taken into consideration, a weighted objective function and iterative optimization logic are designed for single-user and multi-user scenes respectively, signal gain is improved by screening high-weight units and removing high-loss units, interference and crosstalk are suppressed at the same time, the focusing power of the single-user scene is maximized, and the focusing efficiency of the single-user scene is improved. A multi-user scene is combined with a weighted water injection algorithm to realize reasonable power distribution, and system capacity and user fairness are considered. The algorithm adapts to dynamic adjustment of different scenes, the performance and the computing power are balanced, and the defects that an existing algorithm is single in target and poor in adaptability are overcome.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and more specifically, to an electromagnetic wave propagation path optimization system based on a smart metasurface. Background Technology

[0002] As wireless communication technology evolves towards 5G and moves towards the 6G era, users' demands for high-speed, low-latency, and massive connectivity communication continue to rise. High-frequency communication technologies such as millimeter waves and terahertz waves have become key directions for meeting these demands. However, high-frequency electromagnetic waves are susceptible to factors such as obstruction, reflection, scattering, and fading during propagation, leading to a decline in communication link quality. This is especially pronounced in non-line-of-sight (NLoS) scenarios such as densely populated urban areas with high-rise buildings and complex indoor environments, where insufficient signal coverage and severe interference are particularly prominent, seriously restricting the improvement of communication system performance. Traditional electromagnetic wave propagation path optimization methods mainly rely on technologies such as base station deployment adjustment, beamforming, and power control. Among these, base station deployment adjustment is limited by geographical environment and construction costs, making it difficult to achieve accurate coverage across all scenarios. While beamforming technology can achieve directional signal transmission, it relies on active antenna arrays, resulting in high energy consumption and the inability to actively change the propagation environment itself. Power control technology can only alleviate link quality problems by adjusting the transmission power, but it cannot fundamentally solve the path loss problem caused by obstruction.

[0003] Intelligent metasurfaces (RIS), as artificial structures composed of a large number of programmable reflective units, can achieve directional reflection, transmission, or scattering of incident electromagnetic waves by dynamically adjusting the phase, amplitude, and other electromagnetic properties of the units. This provides a novel approach for reconstructing wireless propagation environments. In existing technologies, intelligent metasurfaces have been attempted to be applied to electromagnetic wave path optimization scenarios, but the following shortcomings still exist: (1) Insufficient accuracy in acquiring channel state information. Existing technologies often limit the acquisition of channel state information to the direct link between the base station and the user, or only partially acquire the channel parameters between the base station and the RIS, or between the RIS and the user. They lack comprehensive acquisition of information on the base station-RIS channel, the RIS-user channel, the base station-user direct link channel, and environmental interference. At the same time, the detection accuracy of key parameters such as channel gain and phase offset is limited, resulting in a lack of accurate data support for subsequent path optimization decisions and easy optimization deviations. (2) The phase adjustment algorithm design is imperfect. Existing phase optimization algorithms are mostly aimed at a single target, making it difficult to take into account both the needs of signal gain improvement and interference suppression. In single-user scenarios, the gain differences and contribution weights of each reflection unit of RIS are not fully considered, resulting in some inefficient units occupying computing resources. In multi-user scenarios, crosstalk interference between users is not effectively taken into account, and there is a lack of a power allocation mechanism that takes into account both system capacity and user fairness, making it impossible to achieve global optimization in multi-user scenarios. In addition, the algorithm does not dynamically adjust the optimization strategy for different scenarios, and the optimization timeliness and stability are insufficient in scenarios with rapid channel changes or high-speed user movement.

[0004] To address this, an electromagnetic wave propagation path optimization system based on intelligent metasurfaces has been developed. Summary of the Invention

[0005] To overcome the above-mentioned deficiencies of the prior art, embodiments of the present invention provide an electromagnetic wave propagation path optimization system based on a smart metasurface.

[0006] To achieve the above objectives, the present invention provides the following technical solution: The electromagnetic wave propagation path optimization system based on intelligent metasurfaces includes the following modules: The channel sensing module is used to collect signal status information of the area to be optimized in real time; This includes the first channel state information between the base station and the RIS, the second channel state information between the RIS and the user equipment, the direct link channel state information between the base station and the user equipment, and environmental interference information; The intelligent decision-making module is used to combine the collected signal state information and use a pre-edited electromagnetic wave propagation path optimization algorithm to solve for the optimal RIS deployment location, the optimal reflection phase shift matrix, and the optimal control strategy, which are then encapsulated as control commands. The RIS control module is used to parse control commands and control the deployment location adjustment of the RIS and the regulation of reflection parameters.

[0007] Specifically, the signal status information should be described in detail; The first channel state information includes the channel gain from the base station to the RIS, phase offset, and time delay spread parameters; The second channel state information includes the channel matrix from RIS to the user; Direct link channel state information includes the signal strength, signal-to-noise ratio, and bit error rate of the direct link between the base station and the user equipment. Environmental interference information includes the intensity and frequency distribution of interference signals from other communication systems in the same frequency band.

[0008] Specifically, the logic for determining the optimal RIS deployment location; Define the RIS location coordinates as variables, and take maximizing the average received signal-to-noise ratio of users as the objective function; That is, the maximum value is found by taking the three-dimensional coordinates of the RIS as variables. For the candidate location set pre-constructed by the RIS, the candidate location with the largest objective function value is selected as the optimal RIS deployment location.

[0009] Specifically, the logic for determining the optimal RIS deployment location; Define the RIS location coordinates as variables, and take maximizing the average received signal-to-noise ratio of users as the objective function; That is, the maximum value is found by taking the three-dimensional coordinates of the RIS as variables. For the candidate location set pre-constructed by the RIS, the candidate location with the largest objective function value is selected as the optimal RIS deployment location.

[0010] Specifically, the solution logic for the optimal reflection phase shift matrix; Determining the optimal RIS deployment location Then, the complex channel gain from the base station to the nth RIS unit is extracted from the first signal state information. n = 1, 2, ..., N, where N is the total number of RIS reflection units; expression: ; in This represents the channel gain from the base station to the nth unit at the optimal deployment location. This corresponds to the phase shift; Extract the complex channel gain from the nth unit to the kth user from the second signal state information. ; expression: ; in The channel gain from the nth unit to the kth user at the optimal deployment location. This corresponds to the phase offset.

[0011] Obtain the actual reflection amplitude gain of the nth unit, denoted as Record the gain differences between different units; Define the reflection phase shift matrix: ; in For the phase variable to be optimized, This represents the actual reflection amplitude gain. Based on the difference in amplitude gain of each unit, the total cascaded signal response of the base station-RIS-kth user is: ; Substituting into the amplitude and phase expression for the complex channel gain, we get: ; After identifying the application user scenario, the optimal reflection phase shift matrix is ​​output, including single-user and multi-user scenarios.

[0012] Specifically, high-loss cells are eliminated based on reflection loss; Calculate the reflection loss of the nth unit. That is, through ; Will Cells with losses exceeding a set loss threshold are marked as high-loss cells and removed.

[0013] Specifically, for a single-user scenario, a weighted objective function is constructed; Objective function definition: ; Each of these It is the contribution weight of the nth unit. ; Let the total phase of the reflected signal of the nth unit be equal to the total phase of all units, i.e., there exists a constant C; satisfying... =C, and by rearranging, we can obtain the optimal phase of the nth unit: ; Contribution weights based on different RIS units The process involves filtering low-weight and high-weight units; for low-weight units, their phase is fixed to the default phase; for high-weight units, the optimal phase is extracted and then integrated with the default phase of the low-weight units to generate the optimal reflection phase shift matrix for a single-user scenario.

[0014] Specifically, in a multi-user scenario, the signal-to-noise ratio (SNR) received by the k-th user is factored in with inter-user interference, expressed as: SNR after factoring in inter-user interference. ; The transmit power allocated by the base station to the k-th user; It is the sum of the crosstalk power of all other users to the k-th user; Calculate the global contribution weight of the nth unit in a multi-user scenario. Defined as: Based on global contribution weight Filter low-weight cells and high-weight cells; for low-weight cells, fix their phase to the default phase. For high-weight cells, initial phase ; Indicates a high-weight unit; The total power of the base station is evenly distributed among all users. Based on preset iteration step size, convergence threshold and maximum number of iterations; Initialize the iteration count to 0 and calculate the initial system capacity. ; Power is allocated using a weighted water-filling algorithm, and the effective channel gain of the k-th user is calculated. ; ; Combined with the set user weight factors Construct a power allocation formula for the water injection algorithm to calculate the allocated power. : ;in For Lagrange multipliers; The phase of the high-weighted unit is updated using the gradient ascent method to obtain a new phase matrix; Calculate the updated system capacity ; Calculate the change in capacity

[0015] like If the number of iterations is less than the convergence threshold or the maximum number of iterations is reached, the iteration stops, the optimal phase matrix is ​​output as the optimal reflection phase shift matrix in the multi-user scenario, and the optimal power allocation is integrated.

[0016] Specifically, the logic for determining low-weight units and high-weight units in a single-user scenario; Identify the maximum value of the contribution weight of each unit, i.e., max( The maximum value is multiplied by the preset additional coefficient in the single-user scenario to obtain the reference threshold. Units with contribution weights lower than the reference threshold are marked as low-weight units, and vice versa. The logic for determining low-weight units and high-weight units in multi-user scenarios; Identify the global contribution weight of each unit The maximum value, i.e., max( The reference threshold is obtained by multiplying the maximum value by the preset additional coefficient in the multi-user scenario; units with a global contribution weight lower than the reference threshold are recorded as low-weight units, and vice versa.

[0017] Specifically, the solution logic for the optimal control strategy; Based on the scene association parameters, determine the current scene type; The scenario-related parameters include channel time-varying rate, user movement speed, and interference fluctuation coefficient. Scene types include static stable scenes, dynamic fluctuating scenes, and high-speed moving scenes; Pre-establish control strategies corresponding to different scenario types, determine the scenario type, and extract the corresponding control strategy as the optimal control strategy.

[0018] Specifically, the process of determining the scene type: After weighted fusion of channel time-varying rate, user moving speed and interference fluctuation coefficient, the scene evaluation coefficient is obtained. Three sets of coefficient intervals are set for the scene evaluation coefficient, and each set of coefficient intervals corresponds to a scene type.

[0019] The technical effects and advantages of this invention are as follows: (1) Accurately perceive the channel status and lay the foundation for optimization. The channel perception module comprehensively collects four types of key signal status information, covering the channel parameters of the direct link between the base station and RIS, RIS and users, and base station and users, as well as environmental interference information. Compared with the shortcomings of the existing technology in collecting channel data in a one-sided manner, it provides accurate and comprehensive data support, avoids optimization decision deviation, provides a reliable basis for subsequent path optimization, and effectively solves the problem of insufficient accuracy in obtaining channel status information. (2) The optimized algorithm is highly adaptable and takes into account multiple needs. Weighted objective functions and iterative optimization logic are designed for single-user and multi-user scenarios respectively. By selecting high-weight units and eliminating high-loss units, interference and crosstalk are suppressed while improving signal gain. In single-user scenarios, the focus is on maximizing power, while in multi-user scenarios, the weighted water-filling algorithm is combined to achieve reasonable power allocation, taking into account both system capacity and user fairness. The algorithm is dynamically adjusted to adapt to different scenarios, balancing performance and computing power, and solving the shortcomings of existing algorithms that have a single objective and poor adaptability. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the electromagnetic wave propagation path optimization system based on intelligent metasurfaces according to the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] like Figure 1 As shown, the electromagnetic wave propagation path optimization system modules based on smart metasurfaces are as follows: The channel sensing module is used to collect signal status information of the area to be optimized in real time; This includes the first channel state information between the base station and the RIS, the second channel state information between the RIS and the user equipment, the direct link channel state information between the base station and the user equipment, and environmental interference information; RIS is an abbreviation for Metasurface.

[0023] First channel state information between base station and RIS: The base station transmits a pilot signal in a specific frequency band. After receiving the pilot signal, the RIS obtains the channel gain, phase offset and delay spread parameters from the base station to the RIS through signal strength detection and phase difference calculation. RIS-User Equipment Second Channel State Information: The uplink pilot signal is fed back on the user equipment side. After receiving it, the RIS calculates the channel matrix from the RIS to the user based on the location information of the user equipment. Channel status information of direct link between base station and user equipment: The signal strength, signal-to-noise ratio (SNR) and bit error rate (BER) of the direct link between the base station and user equipment are collected by distributed sensing nodes. If the direct link is blocked by an obstacle, it is determined to be a non-line-of-sight (NLOS) link. Environmental interference information of the area to be optimized is collected using a spectrum analyzer, including the interference signal strength and frequency distribution of other communication systems in the same frequency band.

[0024] The intelligent decision-making module combines the collected signal state information and uses a pre-edited electromagnetic wave propagation path optimization algorithm to solve for the optimal RIS deployment location, the optimal reflection phase shift matrix, and the optimal control strategy, which are then encapsulated as control commands according to the RIS communication protocol. The process of finding the optimal RIS deployment location is as follows: Define the RIS location coordinates (x, y, z) as variables, and take maximizing the average received signal-to-noise ratio of users as the objective function; That is, to find the maximum value using the three-dimensional coordinates of RIS as variables; The formula is: ; in The average received signal-to-noise ratio for all users in the area to be optimized; K represents the total number of users in the region to be optimized, and k is the user ID index; This refers to the base station's transmission power. For the complex channel gain from the base station to the RIS; The complex channel gain from RIS to the k-th user; The power spectral density of Gaussian white noise is set according to the communication environment; This represents the total power of environmental interference signals in the same frequency band. Set position constraints: (Boundary of the region to be optimized); Link constraints: At least one of the base station-RIS and RIS-user links must be a LOS (to avoid severe attenuation of dual NLOS links). Movement constraint: The step size of a single movement of RIS is limited by the maximum displacement of the preset movement mechanism.

[0025] For the candidate position set pre-constructed by RIS, the objective function value is selected. The largest candidate location is selected as the optimal RIS deployment location.

[0026] The process of solving for the optimal reflection phase shift matrix is ​​as follows: Determining the optimal RIS deployment location Then, the complex channel gain from the base station to the nth RIS unit is extracted from the first signal state information. n = 1, 2, ..., N, where N is the total number of RIS reflection units; expression: ; in This represents the channel gain from the base station to the nth unit at the optimal deployment location. This corresponds to the phase shift; Extract the complex channel gain from the nth unit to the kth user from the second signal state information. ; expression: ; in The channel gain from the nth unit to the kth user at the optimal deployment location. This corresponds to the phase offset.

[0027] The actual reflection amplitude gain of the nth unit is obtained through RIS hardware calibration testing, denoted as . Record the gain differences between different units; ideally, assuming that the RIS unit has no loss and no absorption, the value is equal to 1. An incident signal of known power is emitted to the RIS unit, the power of its reflected signal is measured, and the actual reflection amplitude gain is calculated by the power ratio.

[0028] Calculate the reflection loss of the nth unit. That is, through ; Will Cells with losses exceeding a set loss threshold are marked as high-loss cells and removed. Define the reflection phase shift matrix: ; in For the phase variable to be optimized, This represents the actual reflection amplitude gain. Based on the difference in amplitude gain of each unit, the total cascaded signal response of the base station-RIS-kth user is: ; Substituting into the amplitude and phase expression for the complex channel gain, we get: ; This demonstrates the attenuation effect of amplitude gain on signal superposition, avoiding overestimation of performance under ideal assumptions.

[0029] Identify application user scenarios, including single-user scenarios and multi-user scenarios; For a single-user scenario, a weighted objective function is constructed. The total signal received by the user is the superposition of the reflected signals from all RIS units. The signal power is positively correlated with the square of the total signal amplitude. Therefore, the objective function for the total signal amplitude is defined as follows: ; Each of these It is the contribution weight of the nth unit. This represents the unit's ability to contribute power to the total signal; It is the total phase of the reflected signal of the nth unit, which is jointly determined by the base station-RIS link phase, the RIS-user link phase, and the RIS modulation phase; If the total phase of all elements is equal, then there exists a constant C (a phase reference value, which is set to 0 to simplify the calculation). satisfy =C, and by rearranging, we can obtain the optimal phase of the nth unit: ; Contribution weights based on different RIS units Filter out low-weight units; Identify the maximum value of the contribution weight of each unit, i.e., max( The reference threshold is obtained by multiplying the maximum value by the preset additional coefficient in the single-user scenario; the additional coefficient is initially set to 0.2 and can be adjusted in real time. Units with contribution weights lower than the reference threshold are designated as low-weight units, while those with contribution weights higher than the reference threshold are designated as high-weight units. For low-weight units, their phase is fixed to the default phase, such as 0, and they are not included in the optimization. The final superimposed signal is dominated by the high-weighted unit, ensuring maximum received power.

[0030] After extracting the optimal phase from the high-weighted cells, it is integrated with the default phase of the low-weighted cells to generate the optimal reflection phase shift matrix for a single-user scenario.

[0031] In a multi-user scenario, the received signal-to-noise ratio of the k-th user is factored in with inter-user interference (i.e., crosstalk from the reflected signals of other users to the k-th user). The expression is: Signal-to-noise ratio after taking inter-user interference into account ; The transmit power allocated by the base station to the k-th user; It is the sum of the crosstalk power of all other users to the k-th user; As with single-user scenarios, phase optimization should be prioritized for RIS units with high contribution weights to reduce algorithm complexity.

[0032] Calculate the global contribution weight of the nth unit in a multi-user scenario. Defined as: ; The larger the value, the stronger the unit's overall contribution to the multi-user system.

[0033] Identify the global contribution weight of each unit The maximum value, i.e., max( The reference threshold is obtained by multiplying the maximum value by the preset additional coefficient in the multi-user scenario; the additional coefficient is initially set to 0.1 and can be adjusted in real time. Units with a global contribution weight lower than the reference threshold are designated as low-weight units, and vice versa. For low-weight units, their phase is fixed to the default phase, such as 0, and they are not included in the optimization. For high-weight cells, initial phase ; Indicates a high-weight unit; The total power of the base station is evenly distributed among all users. Based on preset iteration step size, convergence threshold and maximum number of iterations; The iteration step size is limited to 0.01-0.1, and the convergence threshold is set to... .

[0034] Initialize the iteration count to 0 and calculate the initial system capacity. ; A weighted water-filling algorithm is used to allocate power, with users in good channel conditions receiving more power. At the same time, the weights are adjusted based on the coverage characteristics of high-weight units. Calculate the effective channel gain for the k-th user. ; ; Combined with the set user weight factors The user weight factor is positively correlated with the coverage ratio of user k by high-weight units. ; Construct a power allocation formula for the water injection algorithm to calculate the allocated power. : ;in These are Lagrange multipliers; the total power constraint must be satisfied. ; To constrain the upper limit of power, the bisection method is used to solve the problem. .

[0035] The phase of the high-weighted unit is updated using the gradient ascent method to obtain a new phase matrix; Gradient Ascent Method Update and Supplement: Updating the phase along the gradient ascent direction requires periodicity, as shown in the formula: Update phase ; The iteration step size, This represents the current phase of the nth unit during the t-th iteration; The gradient value is positive, which increases the phase; the gradient value is negative, which decreases the phase, ensuring adjustment along the direction of maximizing capacity.

[0036] Calculate the updated system capacity ; Calculate the change in capacity ; like If the number of iterations is less than the convergence threshold or the maximum number of iterations is reached, the iteration stops, the optimal phase matrix is ​​output as the optimal reflection phase shift matrix in the multi-user scenario, and the optimal power allocation is integrated. If convergence is not achieved, set t = t + 1 and continue iterating.

[0037] The process of solving the optimal control strategy is as follows: Based on the scene association parameters, determine the current scene type; The scenario-related parameters include channel time-varying rate, user movement speed, and interference fluctuation coefficient. Scene types include static stable scenes, dynamic fluctuating scenes, and high-speed moving scenes; Pre-establish control strategies corresponding to different scenario types; Examples of regulatory strategies: (1) Static stable scenario The channel and interference conditions remain stable over a long period, eliminating the need for frequent updates to the RIS deployment location and phase matrix, thus reducing system computing power consumption.

[0038] Specific regulation process: The optimal RIS deployment location and the optimal reflection phase shift matrix are fixed, the RIS location remains stationary, and the phase matrix does not iterate over time; channel and interference parameters are collected at the default sensing frequency of 10 Hz, and a scene re-determination is performed every 10 minutes: if the parameters still meet the static scene threshold, the current configuration is maintained; if the parameters exceed the threshold, the control strategy for the corresponding scene is switched.

[0039] (2) Dynamic fluctuation scenario The channel and interference conditions change slowly, so the RIS position does not need to be adjusted significantly. The key is to track the channel changes iteratively through the phase matrix to balance performance and computing power. The specific control process is as follows: Based on the movement constraint (the single step size does not exceed the maximum displacement limit), the RIS position is finely adjusted; the difference between the average signal-to-noise ratio of the user at the current position and the signal-to-noise ratio at the optimal position is calculated; if it is greater than 5dB, the single step size is moved in the direction of the optimal position; otherwise, the position remains unchanged; the sensing frequency is increased to 20-30 Hz, and the channel parameters are updated in real time.

[0040] (3) High-speed moving scene Rapid user movement causes rapid channel fading and severe interference fluctuations. Therefore, it is necessary to track channel changes in real time to maximize system performance stability.

[0041] Specific regulation process: Based on user location prediction algorithms (such as Kalman filtering), the system predicts the location distribution of users within the next 0.1 seconds. Aiming to maximize the average signal-to-noise ratio (SNR) of users at the predicted locations, the system dynamically adjusts the RIS (Real-In-Line) position, taking the maximum value for each single movement step to ensure the RIS is always in the optimal coverage area. The sensing frequency is increased to 50 Hz, enabling millisecond-level updates of channel parameters. A high-weight unit priority iteration strategy is adopted: only the top 30% of units with the highest weight are updated in phase, significantly reducing iteration variables. The iteration step size is increased to accelerate phase convergence. Simultaneously, a phase smoothing factor is introduced to avoid signal fluctuations caused by abrupt phase changes.

[0042] After identifying the current scenario type, the corresponding control strategy is matched as the optimal control strategy; Two sets of channel gain data are collected at equal intervals within a time window T, including the channel gain from the base station to the nth RIS unit and the channel gain from the nth RIS unit to the kth user. Utilize the end time After taking the absolute value of the channel gain data, subtract the starting time. The absolute value of the signal gain data is used to obtain the base station-RIS link gain change. and RIS-User Link Gain Change The channel time-varying rate is calculated using the formula. ; The time window is determined by using user location tracking from the second channel state information. Displacement of internal user k Using the formula Calculate the user's movement speed; By extracting interference power from environmental interference information, and using the formula... Calculate the disturbance fluctuation coefficient ; Standard deviation, The average interference power within the time window T; After weighted fusion of channel time-varying rate, user moving speed, and interference fluctuation coefficient, the scenario evaluation coefficient is obtained; After normalizing the channel time-varying rate, user mobility speed, and interference fluctuation coefficient, the scene evaluation coefficient is calculated using the formula. ;in , as well as The weighting coefficients are set, and their sum is one.

[0043] Three sets of coefficient intervals are set for the scene evaluation coefficient, and each set of coefficient intervals corresponds to a scene type; the larger the scene evaluation coefficient, the greater the probability of matching a high-speed moving scene, and vice versa. After matching the scenario evaluation coefficients with the corresponding coefficient ranges, the scenario type is determined and the corresponding regulation strategy is extracted as the optimal control strategy.

[0044] The RIS control module is used to parse control commands and control the deployment location adjustment of the RIS (using movable deployment) and the adjustment of reflection parameters.

[0045] The above formulas are all dimensionless calculations. Dimensionless calculations can be performed using various methods such as standardization, which will not be elaborated here. The formulas are derived from software simulations based on a large amount of collected data, and the preset parameters in the formulas can be set by those skilled in the art according to the actual situation.

[0046] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, ATA hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state ATA hard disk.

[0047] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0048] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0049] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0050] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0051] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0052] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable ATA hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0053] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An electromagnetic wave propagation path optimization system based on intelligent metasurfaces, characterized in that, Includes the following modules: The channel sensing module is used to collect signal status information of the area to be optimized in real time; This includes the first channel state information between the base station and the RIS, the second channel state information between the RIS and the user equipment, the direct link channel state information between the base station and the user equipment, and environmental interference information; The intelligent decision-making module is used to combine the collected signal state information and use a pre-edited electromagnetic wave propagation path optimization algorithm to solve for the optimal RIS deployment location, the optimal reflection phase shift matrix, and the optimal control strategy, which are then encapsulated as control commands. The RIS control module is used to parse control commands and control the deployment location adjustment of the RIS and the regulation of reflection parameters.

2. The electromagnetic wave propagation path optimization system based on intelligent metasurfaces according to claim 1, characterized in that: Detailed explanation of signal status information; The first channel state information includes the channel gain from the base station to the RIS, phase offset, and time delay spread parameters; The second channel state information includes the channel matrix from RIS to the user; Direct link channel state information includes the signal strength, signal-to-noise ratio, and bit error rate of the direct link between the base station and the user equipment. Environmental interference information includes the intensity and frequency distribution of interference signals from other communication systems in the same frequency band.

3. The electromagnetic wave propagation path optimization system based on intelligent metasurfaces according to claim 2, characterized in that: The logic for determining the optimal RIS deployment location; Define the RIS location coordinates as variables, and take maximizing the average received signal-to-noise ratio of users as the objective function; That is, the maximum value is found by taking the three-dimensional coordinates of the RIS as variables. For the candidate location set pre-constructed by the RIS, the candidate location with the largest objective function value is selected as the optimal RIS deployment location.

4. The electromagnetic wave propagation path optimization system based on intelligent metasurfaces according to claim 3, characterized in that: The logic for solving the optimal reflection phase shift matrix; Determining the optimal RIS deployment location Then, the complex channel gain from the base station to the nth RIS unit is extracted from the first signal state information. n = 1, 2, ..., N, where N is the total number of RIS reflection units; expression: ; in This represents the channel gain from the base station to the nth unit at the optimal deployment location. This corresponds to the phase shift; Extract the complex channel gain from the nth unit to the kth user from the second signal state information. ; expression: ; in The channel gain from the nth unit to the kth user at the optimal deployment location. The corresponding phase offset is obtained; the actual reflection amplitude gain of the nth unit is denoted as... Record the gain differences between different units; Define the reflection phase shift matrix: ; in For the phase variable to be optimized, This represents the actual reflection amplitude gain. Based on the difference in amplitude gain of each unit, the total cascaded signal response of the base station-RIS-kth user is: ; Substituting into the amplitude and phase expression for the complex channel gain, we get: ； After identifying the application user scenario, the optimal reflection phase shift matrix is ​​output, including single-user and multi-user scenarios.

5. The electromagnetic wave propagation path optimization system based on intelligent metasurfaces according to claim 4, characterized in that: High-loss cells are eliminated based on reflection loss. Calculate the reflection loss of the nth unit. That is, through ; Will Cells with losses exceeding a set loss threshold are marked as high-loss cells and removed.

6. The electromagnetic wave propagation path optimization system based on intelligent metasurfaces according to claim 4, characterized in that: For a single-user scenario, a weighted objective function is constructed. Objective function definition: ; Each of these It is the contribution weight of the nth unit. ; Let the total phase of the reflected signal of the nth unit be equal to the total phase of all units, i.e., there exists a constant C; satisfying... =C, and by rearranging, we can obtain the optimal phase of the nth unit: ; Contribution weights based on different RIS units Filter low-weight cells and high-weight cells; For low-weight units, their phase is fixed to the default phase. For high-weight units, the optimal phase is extracted and then integrated with the default phase of the low-weight units to generate the optimal reflection phase shift matrix for a single-user scenario.

7. The electromagnetic wave propagation path optimization system based on a smart metasurface according to claim 6, characterized in that: In a multi-user scenario, the signal-to-noise ratio (SNR) received by the k-th user is factored in with inter-user interference, and the expression is: SNR after factoring in inter-user interference. ; The transmit power allocated by the base station to the k-th user; It is the sum of the crosstalk power of all other users to the k-th user; Calculate the global contribution weight of the nth unit in a multi-user scenario. Defined as: Based on global contribution weight Filter low-weight cells and high-weight cells; for low-weight cells, fix their phase to the default phase. For high-weight cells, initial phase ; Indicates a high-weight unit; The total power of the base station is evenly distributed among all users. Based on preset iteration step size, convergence threshold and maximum number of iterations; Initialize the iteration count to 0 and calculate the initial system capacity. ; Power is allocated using a weighted water-filling algorithm, and the effective channel gain of the k-th user is calculated. ; ; Combined with the set user weight factors Construct a power allocation formula for the water injection algorithm to calculate the allocated power. : ;in For Lagrange multipliers; The phase of the high-weighted unit is updated using the gradient ascent method to obtain a new phase matrix; Calculate the updated system capacity ; Calculate the change in capacity; if If the number of iterations is less than the convergence threshold or the maximum number of iterations is reached, the iteration stops, the optimal phase matrix is ​​output as the optimal reflection phase shift matrix in the multi-user scenario, and the optimal power allocation is integrated.

8. The electromagnetic wave propagation path optimization system based on a smart metasurface according to claim 7, characterized in that: The logic for determining low-weight units and high-weight units in a single-user scenario; Identify the maximum value of the contribution weight of each unit, i.e., max( The maximum value is multiplied by the preset additional coefficient in the single-user scenario to obtain the reference threshold. Units with contribution weights lower than the reference threshold are marked as low-weight units, and vice versa. The logic for determining low-weight units and high-weight units in multi-user scenarios; Identify the global contribution weight of each unit The maximum value, i.e., max( The reference threshold is obtained by multiplying the maximum value by the preset additional coefficient in the multi-user scenario; units with a global contribution weight lower than the reference threshold are recorded as low-weight units, and vice versa.

9. The electromagnetic wave propagation path optimization system based on a smart metasurface according to claim 8, characterized in that: The solution logic for the optimal control strategy; Based on the scene association parameters, determine the current scene type; The scenario-related parameters include channel time-varying rate, user movement speed, and interference fluctuation coefficient. Scene types include static stable scenes, dynamic fluctuating scenes, and high-speed moving scenes; Pre-establish control strategies corresponding to different scenario types, determine the scenario type, and extract the corresponding control strategy as the optimal control strategy.

10. The electromagnetic wave propagation path optimization system based on a smart metasurface according to claim 9, characterized in that: The process of determining the scene type: After weighted fusion of channel time-varying rate, user moving speed and interference fluctuation coefficient, the scene evaluation coefficient is obtained. Three sets of coefficient intervals are set for the scene evaluation coefficient, and each set of coefficient intervals corresponds to a scene type.

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