A Wireless Communication Optimization Method and System Based on Reconfigurable Intelligent Surfaces

Abstract

The present invention discloses a wireless communication optimization method and system based on a reconfigurable intelligent surface, which relates to the technical field of wireless communication. The method includes collecting environmental data and network load data, performing a product operation by combining the user density and the network load index to calculate the resource demand index, comparing the resource demand index with a classification threshold range, selecting a BD-RIS architecture, using the Rician fading formula to calculate the corrected channel parameters, and generating a channel matrix; constructing an initial phase shift matrix according to the BD-RIS architecture type, and constructing an objective function based on the initial phase shift matrix; by comprehensively analyzing the network load data and the user density, dynamically setting the RIS architecture to enhance the accurate evaluation and flexible adaptation of the network state, calculating the channel gain benchmark through the Euclidean distance formula and the path loss formula, and optimizing the channel parameters in combination with the phase analysis in the channel state information and the Rician fading formula to reduce signal attenuation and interference, and improve the main channel gain and spectral efficiency.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and in particular, to a wireless communication optimization method and system based on a reconfigurable intelligent surface. Background Art

[0002] With the rapid development of wireless communication technologies, the requirements of the network have become increasingly complex. Especially in environments with high user density and wide coverage, how to optimize the wireless communication network to ensure efficient data transmission has become the focus of research. In current wireless communication systems, high-altitude platform stations (HAPS), as an emerging network infrastructure, have gradually become an important part of future communication networks because they can provide a wider coverage area and lower latency. RIS can optimize the signal propagation path, reduce interference, and increase the channel capacity by precisely controlling the reflection of signals, and has been proven to have significant advantages in multiple scenarios.

[0003] Existing RIS-based wireless communication optimization technologies still face some problems. When considering network load and environmental data, existing solutions rely too much on a single network metric and fail to comprehensively consider complex user distributions and channel conditions. Although RIS can effectively improve the signal quality, how to dynamically adjust the working mode of RIS according to different network environments and load conditions, and how to optimize the communication strategy of high-altitude platform stations. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a wireless communication optimization method and system based on a reconfigurable intelligent surface, which solves the problems of how to dynamically adjust the working mode of RIS according to different network environments and load conditions, and how to optimize the communication strategy of high-altitude platform stations, although RIS can effectively improve the signal quality.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a wireless communication optimization method based on a reconfigurable intelligent surface, which includes:

[0008] Collect environmental data and network load data, perform a product operation by combining the user density and the network load index, calculate the resource demand index, compare the resource demand index with the classification threshold range, select the BD-RIS architecture, use the Rician fading formula to calculate the corrected channel parameters, and generate a channel matrix;

[0009] Construct an initial phase shift matrix according to the BD-RIS architecture type. Based on the initial phase shift matrix, calculate the main channel gain and interference path gain respectively, calculate the spectral efficiency using the Shannon capacity formula, define the objective of maximizing the spectral efficiency, construct an objective function, set an optimization algorithm according to the BD-RIS architecture type to calculate the gradient and update the initial phase shift matrix, and obtain the optimization result;

[0010] Collect feedback data to monitor and adjust the optimization result, construct a visualization interface to display the monitoring result, and store the environmental data and network load data generated by collection and analysis.

[0011] As a preferred solution of the wireless communication optimization method based on the reconfigurable intelligent surface of the present invention, wherein: the collecting of environmental data and network load data to generate a channel matrix includes:

[0012] Use intelligent sensors to collect environmental data. The intelligent sensors include GPS sensors, wireless sensors, spectrum analyzers, temperature sensors, and power meters;

[0013] The environmental data includes user location coordinates, high-altitude platform station location coordinates, channel state information, channel bandwidth, temperature, and signal transmission power;

[0014] The channel state information includes the channel from the base station to the BD-RIS and the channel from the BD-RIS to the user,

[0015] Use network monitoring tools to collect network load data, and use the weighted average method to calculate the density network load index. The network load data includes traffic rate, delay, and packet loss rate data;

[0016] Preprocess the collected environmental data and network load data;

[0017] Calculate the coverage area according to the coverage radius of the high-altitude platform station, calculate the user density using the user density calculation method, and combine the user density and the network load index for multiplication operation to calculate the resource demand index;

[0018] Use the threshold decision method to set the classification threshold range, compare the resource demand index with the classification threshold range, and select the BD-RIS architecture;

[0019] Use the Euclidean distance formula to calculate the Euclidean distance between the user and the high-altitude platform station, use the path loss formula to calculate the path loss, and convert the path loss from dB to a linear value as the gain reference;

[0020] Extract the channel from the channel state information and perform phase analysis. Use the channel component decomposition method to select the path with the largest amplitude and separate it from the channel state information to obtain the line-of-sight component. Subtract the line-of-sight component from the channel state information to obtain the non-line-of-sight component. Use the channel measurement method to set the Rician factor , and use the Rician fading formula to calculate the corrected channel parameters;

[0021] Use the matrix construction method to take the corrected channel parameters as the input of the matrix and generate the channel matrix.

[0022] As a preferred solution of the wireless communication optimization method based on the reconfigurable intelligent surface according to the present invention, wherein: defining the maximized spectral efficiency objective and constructing the objective function, including:

[0023] Use the grouping analysis method to set the number of groups, use the matrix dimension calculation method to calculate the dimension of the channel matrix, and calculate the number of units in each group;

[0024] Construct the initial phase shift matrix according to the BD-RIS architecture type, use the parameter initialization method to set the initial transmission power, and use matrix multiplication to calculate the base station signal component of the corrected channel parameters;

[0025] Calculate the noise power spectral density based on the temperature data using the power spectral density analysis method ;

[0026] Use the channel generation method to obtain the interference channel, and based on the initial phase shift matrix, calculate the main channel gain and the interference path gain respectively;

[0027] Use the complex signal power calculation method to calculate the power gain of the main channel, use vector multiplication to calculate the power gain of the interference channel, use the signal-to-noise ratio formula to calculate the signal-to-noise ratio, and use the signal-to-interference-plus-noise ratio formula to calculate the signal-to-interference-plus-noise ratio;

[0028] Use the Shannon capacity formula to calculate the spectral efficiency, define the maximized spectral efficiency objective, and construct the objective function.

[0029] As a preferred solution of the wireless communication optimization method based on the reconfigurable intelligent surface according to the present invention, wherein: set the optimization algorithm according to the BD-RIS architecture type to calculate the gradient and update the initial phase shift matrix to obtain the optimization result, including:

[0030] Use the default value assignment method to set the initial parameters, set the optimization algorithm according to the BD-RIS architecture type to calculate the gradient and update the initial phase shift matrix, substitute the updated initial phase shift matrix into the objective function to calculate the updated spectral efficiency, use the absolute difference comparison method to calculate the spectral efficiency difference, use the empirical rule to set the convergence threshold, and stop the iteration when the spectral efficiency difference is less than the convergence threshold to obtain the optimization result, including the optimized phase shift matrix and the maximized spectral efficiency.

[0031] As a preferred solution of the wireless communication optimization method based on reconfigurable intelligent surfaces according to the present invention, wherein: collecting feedback data to monitor and adjust the optimization results includes:

[0032] Collecting feedback data and calculating the difference between the feedback data and the optimization results;

[0033] Using statistical methods to set a judgment threshold, comparing the difference with the judgment threshold, and using a PID control algorithm to adjust the difference greater than or equal to the judgment threshold until the adjustment stops when the difference is less than the judgment threshold, and continue to monitor the feedback data.

[0034] As a preferred solution of the wireless communication optimization method based on reconfigurable intelligent surfaces according to the present invention, wherein: constructing a visualization interface to display the monitoring results includes:

[0035] Using the front-end framework React.js to construct a visualization interface to display the monitoring results and the optimization results;

[0036] Allowing users who have passed real-name verification to view.

[0037] As a preferred solution of the wireless communication optimization method based on reconfigurable intelligent surfaces according to the present invention, wherein: storing the environmental data and network load data generated by collection and analysis includes:

[0038] Storing the collected environmental data and network load data, the optimization results and monitoring results generated by analysis into a central database, and setting security access measures. The central database backs up the stored data to the cloud, and regularly performs integrity detection on the stored data and the backup data. After the detection is completed, an integrity detection record is generated and synchronously stored in the central database.

[0039] In a second aspect, the present invention provides a wireless communication optimization system based on reconfigurable intelligent surfaces, including,

[0040] A collection matrix module, configured to collect environmental data and network load data, perform a product operation by combining the user density and the network load index, calculate the resource demand index, compare the resource demand index with the classification threshold range, select a BD-RIS architecture, and use the Rician fading formula to calculate the corrected channel parameters to generate a channel matrix;

[0041] The target optimization module is used to construct an initial phase shift matrix according to the BD-RIS architecture type. Based on the initial phase shift matrix, it calculates the main channel gain and the interference path gain respectively, calculates the spectral efficiency using the Shannon capacity formula, defines the goal of maximizing the spectral efficiency, constructs an objective function, sets an optimization algorithm according to the BD-RIS architecture type to calculate the gradient and update the initial phase shift matrix, and obtains the optimization result;

[0042] The detection and storage module is used to collect feedback data to monitor and adjust the optimization result, construct a visualization interface to display the monitoring result, and store the environmental data and network load data generated by collection and analysis.

[0043] In a third aspect, the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and: when the computer program is executed by the processor, any step of the wireless communication optimization method based on a reconfigurable intelligent surface as described in the first aspect of the present invention is implemented.

[0044] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and: when the computer program is executed by the processor, any step of the wireless communication optimization method based on a reconfigurable intelligent surface as described in the first aspect of the present invention is implemented.

[0045] The beneficial effects of the present invention are as follows: The present invention collects environmental data and network load data, combines the user density and the network load index for multiplication operation to calculate the resource demand index, compares the resource demand index with the classification threshold range, selects the BD-RIS architecture, uses the Rician fading formula to calculate the corrected channel parameters, and generates a channel matrix; constructs an initial phase shift matrix according to the BD-RIS architecture type, based on the initial phase shift matrix, calculates the main channel gain and the interference path gain respectively, calculates the spectral efficiency using the Shannon capacity formula, defines the goal of maximizing the spectral efficiency, constructs an objective function, sets an optimization algorithm according to the BD-RIS architecture type to calculate the gradient and update the initial phase shift matrix, and obtains the optimization result; enhances the accurate evaluation and flexible adaptation of the network state, reduces signal attenuation and interference, and improves the main channel gain and spectral efficiency. Description of the Drawings

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0047] Figure 1 It is a flowchart of the wireless communication optimization method based on a reconfigurable intelligent surface in Embodiment 1.

[0048] Figure 2 Schematic diagram of the wireless communication optimization system based on reconfigurable intelligent surface in Embodiment 1.

[0049] Figure 3 Flow chart based on generating channel matrix in Embodiment 1.

[0050] Figure 4 Flow chart of the wireless communication optimization system module based on reconfigurable intelligent surface in Embodiment 1. Specific implementation manners

[0051] To make the above objects, features, and advantages of the present invention more obvious and understandable, the specific implementation manners of the present invention will be described in detail below with reference to the accompanying drawings of the specification.

[0052] In the following description, many specific details are set forth to facilitate a thorough understanding of the present invention. However, the present invention may be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0053] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation manner of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or alternative embodiments that exclude each other with other embodiments.

[0054] Embodiment 1, referring to Figures 1 to 4 , is the first embodiment of the present invention. This embodiment provides a wireless communication optimization method based on reconfigurable intelligent surface, including the following steps:

[0055] S1. Collect environmental data and network load data, perform a product operation by combining the user density and the network load index, calculate the resource demand index, compare the resource demand index with the classification threshold range, select the BD-RIS architecture, and use the Rician fading formula to calculate the corrected channel parameters to generate a channel matrix;

[0056] Specifically, collecting environmental data and network load data and generating a channel matrix includes:

[0057] Use intelligent sensors to collect environmental data. The intelligent sensors include GPS sensors, wireless sensors, spectrum analyzers, temperature sensors, and power meters;

[0058] The environmental data includes user location coordinates, high-altitude platform station location coordinates, channel state information, channel bandwidth, temperature, and signal transmission power;

[0059] The said channel state information includes the channel from the base station to the BD-RIS and the channel from the BD-RIS to the user;

[0060] Start signal collection on the base station, high-altitude platform station and user terminal, activate the antenna array and signal processor. The wireless sensor configures the beam direction of the antenna array to ensure coverage of the target area, and performs beamforming settings. Transmit pilot signals through the antenna array, receive the pilot signals at the user terminal, use the pilot estimation method to measure the received signal strength and phase offset, and generate channel state information, including the channel from the base station to the BD-RIS and the channel from the BD-RIS to the user. The base station and the high-altitude platform station are responsible for transmitting signals and generating pilot signals, and the user terminal is responsible for receiving signals and estimating channel state information;

[0061] Use network monitoring tools to collect network load data, and use the weighted average method to calculate the density network load index. The network load data includes traffic rate, delay and packet loss rate data;

[0062] Preprocess the collected environmental data and network load data, including denoising using a Gaussian filter and normalizing the environmental data and network load data;

[0063] Calculate the coverage area according to the coverage radius of the high-altitude platform station, calculate the user density using the user density calculation method, combine the user density and the network load index for multiplication operation, and calculate the resource demand index;

[0064] Use the threshold decision method to set the classification threshold range, compare the resource demand index with the classification threshold range, and select the BD-RIS architecture. If the resource demand index is less than the classification threshold range, select the single connection architecture. If the resource demand index is equal to the classification threshold range, select the grouped connection architecture. If the resource demand index is greater than the classification threshold range, select the full connection architecture;

[0065] Use the Euclidean distance formula to calculate the Euclidean distance between the user and the high-altitude platform station, use the path loss formula to calculate the path loss of the Euclidean distance between the user and the high-altitude platform station, and convert the path loss from dB to a linear value as the gain reference. The formula is:

[0066] ,

[0067] where is the gain reference and PL is the path loss;

[0068] Extract the channel in the channel state information and perform phase analysis. Use the channel component decomposition method to select the path with the largest amplitude and separate it from the channel state information to obtain the line-of-sight component. Subtract the line-of-sight component from the channel state information to obtain the non-line-of-sight component. Use the channel measurement method to set the Rician factor , and use the Rician fading formula to calculate the corrected channel parameters. The formula is:

[0069] ,

[0070] where is the corrected channel parameter, is the Euclidean distance between the user and the high-altitude platform station, is the line-of-sight component, calculated through the phase information in the CSI, is the non-line-of-sight component;

[0071] Use the matrix construction method to take the corrected channel parameters as the input of the matrix and generate the channel matrix.

[0072] By combining the user density and the network load index, use the product operation to obtain the resource demand index. This index provides a basis for network optimization decisions. The system can dynamically adjust the resource allocation strategy according to the actual needs of different regions, avoid network overload or resource waste, and improve the overall performance of the network. Based on the comparison between the resource demand index and the classification threshold, selecting different connection architectures can intelligently adjust the network architecture to adapt to different network load requirements. The optimization strategy effectively avoids the situations of over-configuration or under-configuration, improves the utilization rate of network resources. Calculate the path loss between the user and the high-altitude platform station through the Euclidean distance formula, convert the path loss from dB to a linear value as the gain reference, which can calculate the signal attenuation situation more accurately, and thus provide a more accurate prediction model for signal transmission. Through the channel component decomposition method, extract the line-of-sight and non-line-of-sight components from the channel state information, and then use the Rician fading model to correct the channel parameters, which can effectively optimize the channel quality, reduce the impact of signal fading on the communication quality, and improve the stability and transmission rate of the network. Through effective channel measurement, path loss estimation and resource demand prediction, the network architecture and resource allocation strategy can be dynamically adjusted to improve the performance of the wireless communication system and the user experience.

[0073] S2. Construct the initial phase shift matrix according to the BD-RIS architecture type. Based on the initial phase shift matrix, calculate the main channel gain and the interference path gain respectively. Use the Shannon capacity formula to calculate the spectral efficiency, define the objective of maximizing the spectral efficiency, construct the objective function, and set the optimization algorithm according to the BD-RIS architecture type to calculate the gradient and update the initial phase shift matrix to obtain the optimization result;

[0074] Specifically, define the goal of maximizing spectral efficiency and construct the objective function, including:

[0075] Set the number of groups using the grouping analysis method, calculate the dimension of the channel matrix using the matrix dimension calculation method, and calculate the number of elements in each group. The formula is:

[0076] ,

[0077] where is the number of elements, N is the dimension of the channel matrix, and G is the number of groups;

[0078] Construct the initial phase shift matrix according to the BD-RIS architecture type. If the BD-RIS architecture type is a single connection architecture, use the diagonal matrix construction method to initialize the phase shift matrix. If the BD-RIS architecture type is a grouped connection architecture, use the block diagonal matrix construction method to initialize the phase shift matrix. If the BD-RIS architecture type is a full connection architecture, use the identity matrix initialization method to construct a full matrix;

[0079] Set the initial transmit power using the parameter initialization method, and calculate the base station signal component of the corrected channel parameter using matrix multiplication. The formula is:

[0080] ,

[0081] where is the base station signal component of the corrected channel parameter, is the i-th initial phase shift matrix, is the corrected channel parameter from the base station to BD-RIS;

[0082] Calculate the noise power spectral density using the power spectral density analysis method based on temperature data , and the formula is:

[0083] ,

[0084] where K is the Boltzmann constant, T is the temperature, and F is the noise figure provided by the device manufacturer;

[0085] Obtain the interference channel using the channel generation method. Based on the initial phase shift matrix, calculate the main channel gain and the interference path gain respectively. The formula is:

[0086] ,

[0087] ,

[0088] where is the u-th main channel gain, is the corrected channel parameter from BD-RIS to the user The conjugate transpose of is the gain of the v-th interference path, and is the conjugate transpose of the interference channel;

[0089] The power gain of the main channel is calculated using the complex signal power calculation method, the power gain of the interference channel is calculated using vector multiplication, the signal-to-noise ratio is calculated using the signal-to-noise ratio formula, and the signal-to-interference-plus-noise ratio is calculated using the signal-to-interference-plus-noise ratio formula. The formulas are:

[0090] ,

[0091] ,

[0092] where is the signal-to-noise ratio, is the signal-to-interference-plus-noise ratio, is the signal transmission power at time t, is the power gain of the main channel, is the power gain of the interference channel;

[0093] The spectral efficiency is calculated using the Shannon capacity formula. The formula is:

[0094] ,

[0095] where C is the spectral efficiency and B is the channel bandwidth;

[0096] Define the objective of maximizing the spectral efficiency and construct the objective function. The formula is:

[0097] .

[0098] By setting the number of groups through the grouping analysis method and combining with the matrix dimension calculation method to obtain the dimension of the channel matrix, it is possible to reasonably allocate resources and optimize signal processing, ensuring the optimal dimension of the matrix during the signal processing process, effectively improving the processing efficiency and reducing the computational burden. Especially in large-scale communication systems, it can significantly improve the processing ability and real-time performance of the system. According to different types of BD-RIS architectures, different matrix initialization methods are adopted to effectively control the phase of the signal. By optimizing the noise control strategy, the stability of signal transmission can be improved, especially in an environment with high noise, ensuring the robustness and reliability of the system. By calculating the main channel gain and interference path gain through the channel generation method, it is possible to effectively distinguish the main signal and the interference signal. By increasing the signal-to-noise ratio and signal-to-interference-plus-noise ratio, the data transmission rate and signal quality can be effectively improved. Finally, by calculating the spectral efficiency using the Shannon capacity formula and constructing the objective function for maximizing the spectral efficiency, it can provide a clear direction for performance optimization of the system design, contribute to the performance evaluation of the system, and improve the overall transmission rate of the system by adjusting various parameters to maximize the utilization efficiency of the channel.

[0099] Furthermore, according to the BD-RIS architecture type, the optimization algorithm is set to calculate the gradient and update the initial phase shift matrix to obtain the optimization results, including:

[0100] The initial parameters are set using the default value assignment method, and the optimization algorithm is set according to the BD-RIS architecture type to calculate the gradient and update the initial phase shift matrix. If the BD-RIS architecture type is a single-connection architecture, the block coordinate descent method is used for element-by-element optimization for iterative optimization. If the BD-RIS architecture type is a group-connection architecture, the gradient ascent method is used for iterative optimization. If the BD-RIS architecture type is a fully-connected architecture, the Riemannian optimization method is used for iterative optimization. The updated initial phase shift matrix is introduced into the objective function to calculate the updated spectral efficiency. The absolute difference comparison method is used to calculate the spectral efficiency difference. The convergence threshold is set using the empirical rule. When the spectral efficiency difference is less than the convergence threshold, the iteration is stopped to obtain the optimization result, including the optimized phase shift matrix and the maximized spectral efficiency.

[0101] Selecting the appropriate optimization algorithm based on the BD-RIS architecture type ensures optimal performance under each architecture. By selecting targeted optimization strategies, the optimization method can be adjusted according to the specific needs of the system to achieve optimal signal quality and resource utilization. By properly initializing the phase shift matrix, the optimization process converges more quickly, avoiding the computational inefficiencies associated with starting from a disordered initial state. Adjusting the phase shift matrix is crucial for beamforming and multipath signal optimization, effectively improving signal quality and system stability. Improving spectral efficiency not only increases data transmission rates but also supports more users within the same spectrum resources, enhancing network carrying capacity and performance.

[0102] S3. Collect feedback data to monitor and adjust optimization results, build a visual interface to display monitoring results, and store, collect, and analyze environmental data and network load data.

[0103] Specifically, the collection of feedback data to monitor and adjust optimization results includes:

[0104] Collect feedback data and calculate the difference between the feedback data and the optimization results;

[0105] Use statistical methods to set the judgment threshold, compare the difference with the judgment threshold, and use the PID control algorithm to adjust the difference that is greater than or equal to the judgment threshold until the difference is less than the judgment threshold. Stop adjustment and continue monitoring the feedback data.

[0106] The difference calculation provides a quantitative basis for further adjustments, ensuring that the system can promptly identify and respond to potential performance issues. The statistical method makes the adjustment mechanism more precise, avoiding the instability that may be brought about by subjectively setting thresholds. Through the PID control algorithm, the optimization process can gradually approach the optimal solution more accurately, avoiding the situation where the system performance becomes unstable due to over-adjustment or slow response. This closed-loop control process can cope with the changing working environment and external interferences, ensuring that the system is always in the best working state.

[0107] Furthermore, a visual interface is constructed to display the monitoring results, including:

[0108] The visual interface is constructed using the front-end framework React.js to display the monitoring results and optimization results;

[0109] It allows users who have passed real-name verification to access.

[0110] By using React.js to construct the visual interface, its efficient virtual DOM and componentized structure can be fully utilized to ensure the real-time response ability and flexibility of the interface. Through real-name verification, unauthorized users can be effectively prevented from accessing sensitive information, ensuring data security. Through the visual interface, users can perform operations such as data filtering, querying, and report exporting, greatly enhancing the interactivity and user participation of the system.

[0111] Even further, the environmental data and network load data generated by collection and analysis are stored, including:

[0112] The collected environmental data and network load data, along with the optimization results and monitoring results generated by analysis, are stored in the central database, and security access measures are set. The central database backs up the stored data to the cloud and regularly conducts integrity checks on the stored data and backup data. After the checks are completed, integrity check records are generated and synchronously stored in the central database.

[0113] Integrating the scattered monitoring data into a systematic database provides a comprehensive and accurate basis for subsequent analysis and decision-making. Cloud backup not only provides redundant storage of data to prevent data loss caused by hardware failures but also improves the accessibility and recovery speed of data. By regularly conducting integrity checks on the stored data and backup data, it can be ensured that the data has not been tampered with or damaged during storage.

[0114] This embodiment also provides a wireless communication optimization system based on a reconfigurable intelligent surface, including:

[0115] A collection matrix module, which is used to collect environmental data and network load data, perform a multiplication operation by combining the user density and the network load index, calculate the resource demand index, compare the resource demand index with the classification threshold range, select the BD-RIS architecture, use the Rician fading formula to calculate the corrected channel parameters, and generate a channel matrix;

[0116] A target optimization module, which is used to construct an initial phase shift matrix according to the BD-RIS architecture type, calculate the main channel gain and the interference path gain respectively based on the initial phase shift matrix, calculate the spectral efficiency using the Shannon capacity formula, define the target of maximizing the spectral efficiency, construct an objective function, set an optimization algorithm according to the BD-RIS architecture type to calculate the gradient and update the initial phase shift matrix, and obtain the optimization result;

[0117] A detection and storage module, which is used to collect feedback data to monitor and adjust the optimization result, construct a visualization interface to display the monitoring result, and store the environmental data and network load data generated by collection and analysis.

[0118] This embodiment also provides a computer device, which is applicable to the situation of the wireless communication optimization method based on the reconfigurable intelligent surface, and includes: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the wireless communication optimization method based on the reconfigurable intelligent surface proposed in the above embodiment.

[0119] This computer device can be a terminal. This computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of this computer device is used to provide computing and control capabilities. The memory of this computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of this computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of this computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of this computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the shell of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0120] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the method for optimizing wireless communication based on a reconfigurable intelligent surface proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, abbreviated as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, abbreviated as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, abbreviated as EPROM), programmable read-only memory (Programmable Red-Only Memory, abbreviated as PROM), read-only memory (Read-Only Memory, abbreviated as ROM), magnetic memory, flash memory, a magnetic disk or an optical disc.

[0121] In summary, the present invention collects environmental data and network load data, performs a product operation by combining the user density and the network load index to calculate the resource demand index, compares the resource demand index with the classification threshold range, selects the BD-RIS architecture, uses the Rician fading formula to calculate the corrected channel parameters, and generates a channel matrix; constructs an initial phase shift matrix according to the BD-RIS architecture type, calculates the main channel gain and the interference path gain respectively based on the initial phase shift matrix, uses the Shannon capacity formula to calculate the spectral efficiency, defines the objective of maximizing the spectral efficiency, constructs an objective function, sets an optimization algorithm according to the BD-RIS architecture type to calculate the gradient and update the initial phase shift matrix, and obtains the optimization result; enhances the accurate evaluation and flexible adaptation of the network state, reduces signal attenuation and interference, and improves the main channel gain and spectral efficiency.

[0122] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A wireless communication optimization method based on reconfigurable intelligent surfaces, characterized in that: Including, Collecting environmental data and network load data, multiplying the user density and the network load index, calculating the resource demand index, comparing the resource demand index with the classification threshold range, selecting the BD-RIS architecture, using the Rician fading formula to calculate the modified channel parameters, and generating a channel matrix; Constructing an initial phase shift matrix according to the BD-RIS architecture type, calculating the main channel gain and the interference channel gain respectively based on the initial phase shift matrix, calculating the spectral efficiency using the Shannon capacity formula, defining the objective of maximizing the spectral efficiency, constructing an objective function, setting an optimization algorithm according to the BD-RIS architecture type to calculate the gradient and update the initial phase shift matrix, and obtaining the optimization result; Collecting feedback data to monitor and adjust the optimization result, constructing a visualization interface to display the monitoring result, and storing the environmental data and network load data collected and analyzed; The collecting of environmental data and network load data and the generating of the channel matrix include: Collecting environmental data using intelligent sensors, where the intelligent sensors include GPS sensors, spectrum analyzers, temperature sensors, and power meters; The environmental data includes user location coordinates, high-altitude platform station location coordinates, channel state information, channel bandwidth, temperature, and signal transmission power; The channel state information includes the channel from the base station to the BD-RIS and the channel from the BD-RIS to the user, Collecting network load data using network monitoring tools, calculating the network load index using the weighted average method, where the network load data includes traffic rate, delay, and packet loss rate data; Preprocessing the collected environmental data and network load data; Calculating the coverage area according to the coverage radius of the high-altitude platform station, calculating the user density using the user density calculation method, multiplying the user density and the network load index, and calculating the resource demand index; Setting the classification threshold range using the threshold decision method, comparing the resource demand index with the classification threshold range, and selecting the BD-RIS architecture. When the resource demand index is less than the classification threshold range, select the single connection architecture. When the resource demand index is equal to the classification threshold range, select the grouped connection architecture. When the resource demand index is greater than the classification threshold range, select the full connection architecture; Calculating the Euclidean distance between the user and the high-altitude platform station using the Euclidean distance formula, calculating the path loss using the path loss formula, and converting the path loss from dB to a linear value as the gain reference; Extract the channel in the channel state information and perform phase analysis. Use the channel component decomposition method to select the path with the largest amplitude and separate it from the channel state information to obtain the line-of-sight component. Subtract the line-of-sight component from the channel state information to obtain the non-line-of-sight component. Use the channel measurement method to set the Rician factor , and use the Rician fading formula to calculate the corrected channel parameters; Using the matrix construction method to take the modified channel parameters as the input of the matrix and generating a channel matrix; The defining of the objective of maximizing the spectral efficiency and the constructing of the objective function include: Constructing an initial phase shift matrix according to the BD-RIS architecture type, setting the initial transmit power using the parameter initialization method, and calculating the base station signal component of the modified channel parameters using matrix multiplication; Calculating the noise power spectral density using the power spectral density analysis method based on temperature data ; Obtaining the interference channel using the channel generation method, and calculating the main channel gain and the interference channel gain respectively based on the initial phase shift matrix; Calculating the channel gain of the main channel using the complex signal power calculation method, calculating the channel gain of the interference channel using vector multiplication, and calculating the signal-to-interference-plus-noise ratio using the signal-to-interference-plus-noise ratio formula; Calculate the spectral efficiency using the Shannon capacity formula, define the goal of maximizing the spectral efficiency, and construct the objective function.

2. The wireless communication optimization method based on a reconfigurable intelligent surface according to claim 1, characterized in that: Set the optimization algorithm according to the BD-RIS architecture type to calculate the gradient and update the initial phase shift matrix to obtain the optimization result, including: Set the initial parameters using the default value assignment method, set the optimization algorithm according to the BD-RIS architecture type to calculate the gradient and update the initial phase shift matrix, substitute the updated initial phase shift matrix into the objective function to calculate the updated spectral efficiency, use the absolute difference comparison method to calculate the spectral efficiency difference, use the empirical rule to set the convergence threshold, and stop the iteration when the spectral efficiency difference is less than the convergence threshold to obtain the optimization result, including the optimized phase shift matrix and the maximized spectral efficiency.

3. The wireless communication optimization method based on a reconfigurable intelligent surface according to claim 2, wherein: Collect feedback data to monitor and adjust the optimization result, including: Collect feedback data and calculate the difference between the feedback data and the optimization result; Set the judgment threshold using statistical methods, compare the difference with the judgment threshold, and use the PID control algorithm to adjust the difference greater than or equal to the judgment threshold until the difference is less than the judgment threshold and then stop the adjustment and continue to monitor the feedback data.

4. The wireless communication optimization method based on reconfigurable intelligent surfaces according to claim 3, wherein: Construct a visualization interface to display the monitoring result, including: Use the front-end framework React.js to construct a visualization interface to display the monitoring result and the optimization result; Allow users who have passed real-name verification to view it.

5. The wireless communication optimization method based on reconfigurable intelligent surface according to claim 4, characterized in that: Store the environmental data and network load data generated by collection and analysis, including: Store the collected environmental data and network load data, as well as the generated optimization result and monitoring result of the analysis, in the central database, and set security access measures. The central database backs up the stored data to the cloud, and regularly conducts integrity detection on the stored data and the backup data. After the detection is completed, an integrity detection record is generated and synchronously stored in the central database.

6. A wireless communication optimization system based on a reconfigurable intelligent surface, based on the wireless communication optimization method based on a reconfigurable intelligent surface according to any one of claims 1 to 5, characterized in that: Including, A collection matrix module, which is used to collect environmental data and network load data, perform a product operation by combining the user density and the network load index, calculate the resource demand index, compare the resource demand index with the classification threshold range, select the BD-RIS architecture, and use the Rician fading formula to calculate the corrected channel parameters to generate a channel matrix; A target optimization module, which is used to construct an initial phase shift matrix according to the BD-RIS architecture type, calculate the main channel gain and the interference channel gain based on the initial phase shift matrix, calculate the spectral efficiency using the Shannon capacity formula, define the goal of maximizing the spectral efficiency, construct the objective function, set the optimization algorithm according to the BD-RIS architecture type to calculate the gradient and update the initial phase shift matrix to obtain the optimization result; A detection and storage module, which is used to collect feedback data to monitor and adjust the optimization result, construct a visualization interface to display the monitoring result, and store the environmental data and network load data generated by collection and analysis.

7. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the wireless communication optimization method based on the reconfigurable intelligent surface according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the wireless communication optimization method based on the reconfigurable intelligent surface according to any one of claims 1 to 5.

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